{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "960ceacb-3c3f-484c-95b9-172bd009b1a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a generic code for drawing a state's Home Districts for neutral map estimation\n",
      "In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\n"
     ]
    }
   ],
   "source": [
    "#  THIS IS THE PRIMARY VERSION TO USE FOR ALL STATES  #See MO for orig 2/10/24.  This one from NJ 4-21-24\n",
    "print(\"This is a generic code for drawing a state's Home Districts for neutral map estimation\") #Jan'24\n",
    "print(\"In this code, we use the term 'tract' generically to refer the base building block, usually vtd's\")\n",
    "import shapely\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "from shapely.ops import nearest_points, transform\n",
    "from shapely.affinity import translate, scale\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "from scipy.optimize import minimize, minimize_scalar\n",
    "import math\n",
    "import time\n",
    "import ast\n",
    "# from HDmethods import *   #I want to implement this, but my HDmethods require shapely functions\n",
    "# see https://stackoverflow.com/questions/66877728/proper-way-to-use-import-when-calling-external-function for a future workaround\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ab4a3d5-f4e7-4aec-b81c-0e786cd35b57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getWeightedAvgAndSD(LIST, WEIGHTS):  #from IN\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def squish(shapeList, cutLINE, farLINE, newFarLINE):\n",
    "    \"\"\"\n",
    "    This method compresses a list of shapes lying between the cutLINE and the farLINE)\n",
    "    such that the Polygons now lie between the cutLINE and the newFarLINE, which is closer to the cutLINE\n",
    "    Used to compress state outcroppings into a more compact state representation.\n",
    "     (I tried doing this point by point (see #'s), but this overdistorted geometries.)\n",
    "      (#This was a manual alternative to shapely.affinity.scale and .translate operations)\n",
    "       So instead, we run this AFTER established untransformed map topology, and \n",
    "        we simply scale and translate each unit.  This loses true connectivity.\n",
    "        Scaling is all lengths scale by compression of distance\n",
    "    The cut, far, and newFar LineString segments are preferably parallel\n",
    "    The method returns the modified shapes of all pollys\n",
    "    \"\"\"\n",
    "    if cutLINE.intersects(farLINE) or cutLINE.intersects(newFarLINE):\n",
    "        print(\"cutLine,farLine, newFarLine were\",cutLINE, farLINE, newFarLINE)\n",
    "        raise Exception(\"ERROR: the proposed cutLine intersects the current or proposed far line\")\n",
    "    newPollys = list()\n",
    "    for polly in shapeList:\n",
    "        pollyCP = polly.centroid\n",
    "        cutPt =     nearest_points(cutLINE,   pollyCP)[0]\n",
    "        farPt =     nearest_points(farLINE,   pollyCP)[0]\n",
    "        newFarPt =  nearest_points(newFarLINE,pollyCP)[0]\n",
    "        curr_cutX, curr_cutY =            pollyCP.x - cutPt.x, pollyCP.y -  cutPt.y\n",
    "        currFar_CutX , currFar_CutY  =    farPt.x - cutPt.x, farPt.y   -  cutPt.y\n",
    "        new_currFarX, new_currFarY =      newFarPt.x - farPt.x, newFarPt.y - farPt.y\n",
    "        newFar_CutX,  newFar_CutY =       newFarPt.x - cutPt.x, newFarPt.y   -  cutPt.y\n",
    "        distanceRatio = newFarPt.distance(cutPt)/farPt.distance(cutPt) #scale area ^length^2\n",
    "        XOFF,YOFF = 0., 0.\n",
    "        XFACT, YFACT = 1., 1.  #default = no stretch\n",
    "        # basic equation: (new-cut)/(curr-cut) = (newFar-cut)/(currFar - cut)\n",
    "        #  or: (new-curr)  = (curr-cut)*(newFar-currFar)/(currFar - cut)   # -1 to both sides\n",
    "        if currFar_CutX != 0:\n",
    "            XOFF =  curr_cutX * new_currFarX / currFar_CutX\n",
    "            XFACT =              newFar_CutX / currFar_CutX\n",
    "        if currFar_CutY != 0:\n",
    "            YOFF =  curr_cutY * new_currFarY / currFar_CutY\n",
    "            YFACT =              newFar_CutY / currFar_CutY\n",
    "        newPolly = translate(polly,xoff=XOFF,yoff=YOFF)\n",
    "        newPolly = scale(newPolly, xfact=XFACT, yfact=YFACT, origin='centroid')\n",
    "        newPollys.append( newPolly )       \n",
    "    return newPollys\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "#  The below four methods are TO SNAP to HD SHAPES without any solving / iteration\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    The optional xScale parameter is the ratio of longitude to latitude lengths scales (<<1 for far from equator)\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def buildPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a 4-tri polygon from four HD wedges emanating from the centerpoint\n",
    "    Pt = [Point(0,0)]*12                      #xScale has same meaning as in buildWedge\n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ]  \n",
    "        Pt[nW*2] =   Point(CP.x + RADII[nW]/XSCALE*math.cos(ccwAngle), CP.y + RADII[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(CP.x + RADII[nW]/XSCALE*math.cos( cwAngle), CP.y + RADII[nW]*math.sin( cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def buildArcPoly(CP, RADII, ANGLES, XSCALE = 1.0):  #reconstructs a fuller polygon from four HD wedges emanating from the centerpoint\n",
    "    twoPi = 2. * 3.1415926   #xScale has same meaning as in buildWedge\n",
    "    POINTS = list()                   \n",
    "    for nW in range(4): \n",
    "        ccwAngle = ANGLES[nW] % twoPi                 #these two are the angles at start and end of nth wedge\n",
    "        cwAngle =  ANGLES[ int((nW+1)%4) ] % twoPi \n",
    "        midAngle = [0.75*ccwAngle + 0.25*cwAngle , 0.25*ccwAngle + 0.75*cwAngle ]  #avoid s sampling cone center for wide angles\n",
    "        if abs (ccwAngle - cwAngle) > 3.1415926 :  #angles straddle angle=0\n",
    "            midAngle = [0.75*(ccwAngle - twoPi) + 0.25*cwAngle , 0.25*(ccwAngle -twoPi) + 0.75*cwAngle ]\n",
    "        angList = [ccwAngle] + midAngle + [cwAngle]\n",
    "        for ang in angList:\n",
    "            POINTS.append(Point(CP.x + RADII[nW]/XSCALE*math.cos(ang), CP.y + RADII[nW]*math.sin(ang) ) )\n",
    "                          \n",
    "    POLLY = Polygon(POINTS)\n",
    "    return POLLY\n",
    "\n",
    "def getPolyPop(POLLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points contained by a polygon\n",
    "    WPOP, WLIST = 0., list()\n",
    "    for tt in CANDIDATELIST:\n",
    "        if POLLY.contains(TRACTCP[tt]):\n",
    "            WPOP += TRACTPOP[tt]\n",
    "            WLIST.append(tt)\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonCPP(POLLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county in getPolyPop\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if POLLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getNonHCunits(POLLY, HC, UNITLIST, UNITCP, UNITPOP, ALLFUSEDCOUNTIES, AREAFRAC, COUNTYGEOM, \n",
    "                  NEIGHBORCOUNTYLIST, COUNTYUNITLIST):\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a POLLY polygon for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county.  This should be more efficient than probing every unit in the map\n",
    "    After each county is checked, it goes into the checked list so we don't re-check, then we recursively probe county neighbors\n",
    "    \"unit counties\" are added as whole units if a sufficient area fraction is captured.  For non-unitCs, we probe each component vtd / tract\n",
    "    \"\"\"\n",
    "    UPOP, ULIST = 0., list()\n",
    "    checkedClist = [HC]   #dynamic list of counties we've already checked.  We probed the home county before we called this method\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        #1/9/24 - DO WE NEED TO MODIFY THE ORDER BELOW TO AVOID CREATING HOLES AND ENCLAVES ??  #\n",
    "        \n",
    "        for c in latestClist:\n",
    "            for cc in list( set(NEIGHBORCOUNTYLIST[c]).difference(set(ALLFUSEDCOUNTIES)) ):\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        if cc+0.5 in UNITLIST:\n",
    "                            if POLLY.intersection(COUNTYGEOM[cc]).area >=  AREAFRAC[cc] * COUNTYGEOM[cc].area :\n",
    "                                intersectedClist.append(cc)   #for unit counties, intersections count only if they capture sufficient area\n",
    "                                newClist.append(cc)\n",
    "                                UPOP += UNITPOP[UNITLIST.index(cc+0.5)]\n",
    "                                ULIST.append( UNITLIST.index(cc+0.5) )\n",
    "                        else:                           \n",
    "                            intersectedClist.append(cc)\n",
    "                            newClist.append(cc)\n",
    "                            if POLLY.contains(COUNTYGEOM[cc]):\n",
    "                                unitsToAdd = COUNTYUNITLIST[cc]\n",
    "                                UPOP += np.sum(  [ UNITPOP[uu] for uu in unitsToAdd ]  )\n",
    "                                ULIST += unitsToAdd\n",
    "                            else:\n",
    "                                for uu in COUNTYUNITLIST[cc]:\n",
    "                                    if POLLY.contains(UNITCP[uu]):\n",
    "                                        UPOP += UNITPOP[uu]\n",
    "                                        ULIST.append(uu)\n",
    "        latestClist = newClist.copy()\n",
    "        \n",
    "    return UPOP, ULIST\n",
    "\n",
    "def clusterSwell(CP_, CCBgeom, CCBpop, allUnits, unitCP, unitPop, unitNbrs, borderUnits, TGTPOP):\n",
    "    \"\"\"\n",
    "    This method grows a Home District by \"swelling\" from a corner county cluster.  First, we add the full cluster,\n",
    "     then we add closest neighbor units to the home district centerpoint CP_ until we reach the TGTPOP\n",
    "     new for MN 4/7/24 - don't enforce contiguity here -- too slow -- fix in cleanup stage instead\n",
    "     \"\"\"\n",
    "    hd_CCBdist = [CP_.distance(geo) for geo in CCBgeom]\n",
    "    CCBno = hd_CCBdist.index(np.min(hd_CCBdist))  #ID's the closest cluster to this HD center.  Should contain the HD, but rarely will not\n",
    "    unitNo = allUnits.index(CCBno+0.25)\n",
    "    newList, prevList, addedList, addedPop = [unitNo], [unitNo], [unitNo], unitPop[unitNo]  #seed with the cluster pop and unit\n",
    "    while addedPop < 0.99 * TGTPOP and len(newList) > 0:\n",
    "        tryList = getAdjoiners(addedList,unitNbrs)\n",
    "        tryDist = [ CP_.distance(unitCP[UU]) for UU in tryList ]\n",
    "        idx = np.argsort(tryDist)\n",
    "        idxNo, newList = 0, list()\n",
    "        haveNotAdded = True\n",
    "        while idxNo < len(tryList) and haveNotAdded:\n",
    "            UU = tryList[idx[idxNo]]\n",
    "            if unitPop[UU] + addedPop < 1.01 * TGTPOP :  #and wontEnclave(UU, addedList, unitNbrs, borderUnits) :  #we'll post-fix contig'y\n",
    "                newList.append(UU)\n",
    "                addedList.append(UU)\n",
    "                addedPop += unitPop[UU]\n",
    "                haveNotAdded = False\n",
    "            idxNo +=1\n",
    "        prevList = newList.copy()\n",
    "        \n",
    "    return addedPop, addedList\n",
    "            \n",
    "def getFakeHC(HDPOLLY, HC, CCBlist, countyGeom, MAP) :\n",
    "    \"\"\"\n",
    "    This method is for setting a fake home county for counties in corner clusters, so that county neighbors can be found budding from the \"home county\"\n",
    "    We pick the county in the cluster closest to the map center and intersecting the HDpoly.  This will be an active county on the cluster boundary   \n",
    "    \"\"\"\n",
    "    MAPcenter = MAP.centroid\n",
    "    for L in CCBlist:\n",
    "        if HC in L:\n",
    "            distList, cList = list(), list()\n",
    "            for C in L:\n",
    "                if HDPOLLY.intersects(countyGeom[C]):\n",
    "                    distList.append(countyGeom[C].distance(MAPcenter))\n",
    "                    cList.append(C)\n",
    "    fakeHC = cList[distList.index(np.min(distList)) ]\n",
    "    return fakeHC\n",
    "\n",
    "def getLongDist(CP1, CP2, xScale = 1.0): #distance between two points with EW distance scaled down by latitude\n",
    "    #LAT = MAP.centroid.y\n",
    "    #xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "    dist = ( xScale*xScale * (CP1.x - CP2.x)*(CP1.x - CP2.x)  +  (CP1.y - CP2.y)*(CP1.y - CP2.y) ) **0.5 \n",
    "    return dist\n",
    "\n",
    "# THESE ARE THE METHODS USED IN SOLVING HD SHAPES\n",
    "def buildWedge(CP,STARTANGLE, ENDANGLE, RR,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    This method creates triangular wedges from a centerpoint, wedge start and end angles, and wedge radius.\n",
    "    It passes back a SINGLE wedge polygon\n",
    "    \"\"\"\n",
    "    A0 = STARTANGLE\n",
    "    A1 = ENDANGLE\n",
    "    PT1 = Point(CP.x + RR/XSCALE*math.cos(A0),  CP.y + RR*math.sin(A0) )\n",
    "    PT2 = Point(CP.x + RR/XSCALE*math.cos(A1),  CP.y + RR*math.sin(A1) )\n",
    "    WEDGEPOLY = Polygon( [CP,PT1, PT2 ])\n",
    "    return WEDGEPOLY\n",
    "\n",
    "def getCountyWP(T, WEDGEPOLY, TRACTCP, TRACTPOP, CANDIDATELIST ):  #simple capture of pop points in a wedge excluding a point\n",
    "        WPOP, WLIST = 0., list()\n",
    "        for tt in CANDIDATELIST:\n",
    "            if tt != T and WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                WPOP += TRACTPOP[tt]\n",
    "                WLIST.append(tt)\n",
    "        return WPOP, WLIST\n",
    "\n",
    "def getNonCWP(WEDGEPOLY, HC, TRACTCP, TRACTPOP, COUNTYGEOM, COUNTYPOP, COUNTYTRACTLIST, NEIGHBORCOUNTYLIST) : #nonCounty wedgePop\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by an infinite polygonal wedge for counties contiguous with the Home County (no hop-overs)\n",
    "    , EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def isIncludedA(STARTa, ENDa, TESTa): #determines if a test angle is between a start and end angle (True)\n",
    "    \"\"\"\n",
    "    The start angle must be less than the end angle when placed on the [0, 2 pi] interval  (ccw convention)\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    STARTa, ENDa, TESTa = STARTa % (2.*pi), ENDa % (2.*pi), TESTa % (2.*pi)  #place on the 2pi interval\n",
    "    isIncludedA = False\n",
    "    if STARTa  > ENDa :  #included angle straddles east\n",
    "        if   TESTa  >= STARTa or TESTa < ENDa :  #near-east between the two\n",
    "            isIncludedA = True\n",
    "    else:\n",
    "        if TESTa >= STARTa and TESTa < ENDa :  #normal case\n",
    "            isIncludedA = True\n",
    "    return isIncludedA\n",
    "\n",
    "def getNonCWP_c(STARTANGL, ENDANGL, HC, TRACTCP,TRACTPOP,COUNTYGEOM,COUNTYPOP,COUNTYTRACTLIST,\n",
    "                                    NEIGHBORCOUNTYLIST, UUDIST, UUANGLE, WEDGEPOLY) :\n",
    "    \"\"\"\n",
    "    This method computes the total pop captured by a polygonal wedge for counties contiguous with the Home County (no hop-overs),\n",
    "    EXCLUDING the home county for the centerpoint of the wedge.  This should be more efficient than going tract-by-tract\n",
    "    After each county is checked, it goes into the checked list so we don't re-check.  Next round = nonchecked neighbors of current round\n",
    "    Unlike its getNonCWP vanilla parent, this one uses angles rather than infinite wedges for units (still wedges for counties)\n",
    "    \"\"\"\n",
    "    WPOP, WLIST = 0., list()\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if WEDGEPOLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "                        if WEDGEPOLY.contains(COUNTYGEOM[cc]):\n",
    "                            WPOP += COUNTYPOP[cc]\n",
    "                            WLIST += COUNTYTRACTLIST[cc]\n",
    "                        else:\n",
    "                            for tt in COUNTYTRACTLIST[cc]:\n",
    "                                if isIncludedA(STARTANGL, ENDANGL, UUANGLE[tt]): #WEDGEPOLY.contains(TRACTCP[tt]):\n",
    "                                    WPOP += TRACTPOP[tt]\n",
    "                                    WLIST.append(tt)\n",
    "        #below is temp debug\n",
    "        #print(len(intersectedClist),WPOP, len(WLIST),\"counties probed, pop, len(tractList)\")\n",
    "        latestClist = newClist.copy()\n",
    "    return WPOP, WLIST\n",
    "\n",
    "def getExitAngle(CP,WEDGEPOLY, MAP, A0, A1):\n",
    "    \"\"\"\n",
    "    This code determines the orientation angle from a CenterPoint to the intersection of a wedge with an exterior boundary\n",
    "    If an intersection is not found, we just take the average angle of the wedge start/stop angles A0, A1 as this orientation angle\n",
    "    MAP must be a single polygon for this to work in the revised code (tho could run thru all polygons in MAP if a multipolygon)\n",
    "    \"\"\"\n",
    "    EXITANGLE = 0.5* (A0 + A1) #this will be the angle from the x-axis to the exit beeline\n",
    "    if WEDGEPOLY.intersects(MAP.exterior):\n",
    "        edgeLine = WEDGEPOLY.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "        closePoint = nearest_points(edgeLine,CP)[0]\n",
    "        dx = closePoint.x - CP.x       #this and below lines were indented in original code***\n",
    "        dy = closePoint.y - CP.y\n",
    "        EXITANGLE =  pi/2. * np.sign(dy)  #default in case dx=0\n",
    "        if (dx != 0. ):\n",
    "            EXITANGLE = math.atan(dy/dx) + random.uniform(-0.01,0.01) #add wiggle to avoid exact NESW orientation in gridded states\n",
    "            if dx < 0. :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                EXITANGLE = pi + EXITANGLE\n",
    "    return EXITANGLE # this reorients 0th wedge to face boundary's closest point\n",
    "\n",
    "def getNewAngles(mWP, tWP, ADP, LEVEL_L, MINADJRATIO, MAXANGLE, MAXANGLERATIO, nUNFILLEDWEDGES): \n",
    "    \"\"\"\n",
    "    This method classifies Home Districts based on how their post-reoriented equi-angle max wedge pops stack up vs targets,\n",
    "     then changes wedge angles in some situations to pick up more population along the boundary\n",
    "    If there is one wedge that will fall short of a quarter district pop, then we adjust angles if it is sufficiently short    \n",
    "    (Using \"HD2\" code, the opposite wedge's pop will be constrained as well.)\n",
    "    If there are two opposite-facing constrained wedges, we do not adjust angles\n",
    "    If there are two adjacent constrained wedges, we classify as a corner (no angle change) if the adjacent wedge is sufficiently constrained,\n",
    "      otherwise we treat as a single constrained wedge\n",
    "    If there are three constrained wedges, the angles are unchanged; we use the 4th wedge to pick up all pop\n",
    "    If all four wedges are constrained, there is an error and we raise a flag.\n",
    "    mWP is the quadlist of maxWedgePops we would get by extending each wedge to the MAP boundary\n",
    "    tWP is the quadlist of targetWedgePops\n",
    "    LEVEL_L is the non-dimensional distance to the boundary at which we no longer adjust wedge angles to drive more near-boundary pop\n",
    "    MAXANGLERATIO is the max ratio of the wide angle (facing the boundary) to the normal angle (e.g. 90deg for four wedges) ...\n",
    "    but this is truncated near-boundary to MAXANGLE (e.g. 1.8 pi/2 instead of increasing all the way to 1.9 pi/2)\n",
    "      NOTE THAT this code does not consider nearly-shorted wedges -- those are a separate method called later if all mWP's > tWP's\n",
    "    \"\"\"   \n",
    "    isChange = False   #default; we are NOT changing angles\n",
    "    printDebuggg = False\n",
    "    NWEDGES = len(mWP)\n",
    "    avgWedgeAngle = 2.*math.pi / NWEDGES\n",
    "    minW = mWP.index(np.min(mWP))  #index of the wedge with the lowest max wedge pop\n",
    "    oppW = int( int(minW + NWEDGES/2) % NWEDGES )  #and its opposing wedge\n",
    "    Lstar = ( mWP[minW] / (0.25*ADP) )**0.5  #shortest wedge's nondim'l distance to boundary\n",
    "    \n",
    "    case = \"keep same wedge angles\" #default; no unfillable wedges or all but one are unfillable\n",
    "    if nUNFILLEDWEDGES >= NWEDGES:\n",
    "        raise Exception(\"ERROR! ALL WEDGES ARE CONSTRAINED for tract\",t,\".  IMPOSSIBLE!!\")\n",
    "    if nUNFILLEDWEDGES == 1:\n",
    "        case = \"constrain opp wedge\"\n",
    "        if Lstar >= levelL :\n",
    "            case = \"keep same wedge angles\"  #not close enough to boundary to distort HD shape\n",
    "    if nUNFILLEDWEDGES == 0 or nUNFILLEDWEDGES == NWEDGES - 1:  #far from boundary or with only one wedge w/large pop\n",
    "        case = \"keep same wedge angles\"   \n",
    "    if nUNFILLEDWEDGES == 2:  #must determine if opposite or adjacent           \n",
    "        if mWP[oppW] < tWP[oppW]:  #shorted wedges oppose each other, so ...\n",
    "            case = \"keep same wedge angles\" #...the HD shape will naturally widen to pick up adjacent pop\n",
    "        else:  #we have 2 adjacent (non-opposing) shorted wedges... but how shorted?  ID the adjacent wedge\n",
    "            for nW in range(NWEDGES):\n",
    "                if mWP[nW] < tWP[nW] and nW != minW :\n",
    "                    adjW = nW  #this is the adjacent wedge\n",
    "                    adjRatio = mWP[adjW] / tWP[adjW]\n",
    "            if adjRatio < minAdjRatio:  #we're close to a corner (this adjacentWedge is significantly constrained)\n",
    "                case = \"keep same wedge angles\"\n",
    "                if printDebuggg :\n",
    "                    print(\"Not adjusting wedge angles for tract\",t,\"as 2nd-sparsest wedge\",adjW,\"has pop\",mWP[adjW] )\n",
    "            else:\n",
    "                case = \"constrain opp wedge\"  #we will set the angles and target pops as if the adj wedge were not constrained\n",
    "    \n",
    "    if case == \"keep same wedge angles\":\n",
    "        isChange = False\n",
    "        WEDGEANGLE = [avgWedgeAngle for w in range(NWEDGES) ]\n",
    "\n",
    "    if case == \"constrain opp wedge\":  #Here, we modify wedge angles.  MWP's will be recalc'd outside the method\n",
    "        isChange = True  \n",
    "        wideAngle = min(MAXANGLE, avgWedgeAngle*max(  1,( 1.+(MAXANGLERATIO-1.)*(LEVEL_L - Lstar)/(LEVEL_L - 0.) )  ) )\n",
    "        WEDGEANGLE = [(2.*pi - 2. * wideAngle)/ (NWEDGES - 2.) for w in range(NWEDGES) ]  \n",
    "        WEDGEANGLE[minW] = wideAngle\n",
    "        WEDGEANGLE[oppW] = wideAngle\n",
    "    \n",
    "    return isChange, WEDGEANGLE\n",
    "\n",
    "def rebalanceTWPs(MWP, TWP, BARREDLIST=list()):\n",
    "    \"\"\"\n",
    "    This method iteratively increases targetWedgePops to accommodate wedges that have maxWedgePop < targetWedgePop.\n",
    "    The wedgePop gap is distributed equally among wedges that have some capacity and are not BARRED (e.g. an opposite wedge in HD2 method)\n",
    "     (If this pushes some wedges over capacity, this will be fixed in a later run through loop inside this method)\n",
    "    We also flag which wedges \"will fill\" = have sufficient capacity after the rebalancing of the targets to not use the entire maxWedgePoly\n",
    "    \"\"\"\n",
    "    DEBUGrTWP = False\n",
    "    NWEDGES = len(MWP)\n",
    "    unorderedCapacity = [MWP[w] - TWP[w] for w in range(NWEDGES) ]\n",
    "    idx = np.argsort(unorderedCapacity)\n",
    "    #for nn in range(NWEDGES):\n",
    "    #    print(MWP[idx[nn]])\n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        for nn in range(NWEDGES):\n",
    "            print(\"barred list, orig MWP, TWP\",BARREDLIST, r3(MWP[nn]),r3(TWP[nn]) )\n",
    "    \n",
    "    for nn in range(NWEDGES):  #going from least to most maxWedgePop here ...\n",
    "        nW = idx[nn]\n",
    "        if MWP[nW] < TWP[nW]: #need to redistribute extra target to higher-capacity wedges ...\n",
    "            wedgePopGap = TWP[nW] - MWP[nW]\n",
    "            TWP[nW] = MWP[nW]  #max out this wedge\n",
    "            nReceivers = 0.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....\n",
    "                    nReceivers += 1.\n",
    "            for WW in range(NWEDGES):\n",
    "                if MWP[WW] > TWP[WW] and WW not in BARREDLIST:  #this wedge could take at least a little more ....                    \n",
    "                    TWP[WW] += wedgePopGap / nReceivers  #... so give it equal share of the gap, even if this goes over; we'll correct in later loop\n",
    "                    \n",
    "    WILLFILL = [1]*NWEDGES\n",
    "    for nW in range(NWEDGES):\n",
    "        if MWP[nW] < 1.001* TWP[nW]:  #required pop nearly or truly requires the entire wedge\n",
    "            WILLFILL[nW] = 0 \n",
    "    if np.min(TWP) < 0.99 * np.average(TWP) and DEBUGrTWP:  #debug\n",
    "        print(\"adjusted TWP's are\",TWP)\n",
    "    return TWP, WILLFILL\n",
    "\n",
    "def solveWedge(nontractTWP,t, hC, STARTANGLE, ENDANGLE, MAXD, TOLERPOPS, tractCP, tractPop,\n",
    "               countyTractList, countyGeom, countyPop, neighborCountyLIST,XSCALE=1.0):\n",
    "    \"\"\"\n",
    "    #### NO LONGER USED; SEE FASTER SOLVEWEDGEB BASED ON UNIT-UNIT DISTANCES  *********\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop (which excludes the home tractPop)\n",
    "    The wedge has a fixed starting and ending angle.  Its maximum diameter is angle-related to max diameter of the state map\n",
    "    It uses bisection, NOT scipy minimize to minimize the square error in the wedge pop vs. target\n",
    "    The method passes back the final wedge pop and list of tracts in the wedge\n",
    "    \"\"\"\n",
    "    chgLoopNo, maxLoopNo = 10., 22.  #when we will start relaxing the pop tolerance, and when we give up\n",
    "    includedAngl = ENDANGLE - STARTANGLE\n",
    "    guessedR = MAXD / math.cos(0.5*includedAngl)  #max possible wedge radius, accounting for possible wide angle\n",
    "    popTol = TOLERPOPS[0]  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    #                      We loosen toward half the MAX tractPop if not converging\n",
    "    \n",
    "    offsetPop = 88888888.  #to force a reduction the first time through the loop\n",
    "    dr = guessedR\n",
    "    loopNo = 0\n",
    "    while abs(offsetPop) > popTol and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        popTol = max(TOLERPOPS[0], TOLERPOPS[0] + (TOLERPOPS[1] - TOLERPOPS[0]) * (loopNo - chgLoopNo) / (maxLoopNo - chgLoopNo) )\n",
    "        dr = 0.5*dr\n",
    "        guessedR -= dr*np.sign(offsetPop)\n",
    "        \n",
    "        guessedPoly = buildWedge(tractCP[t],STARTANGLE, ENDANGLE, guessedR,XSCALE) \n",
    "        iCP, iClist =  getCountyWP(t,guessedPoly, tractCP, tractPop, countyTractList[hC])\n",
    "        nonCP, nonClist = getNonCWP(guessedPoly, hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyLIST)\n",
    "        offsetPop = iCP + nonCP - nontractTWP\n",
    "        #print(t,loopNo,r5(guessedR),int(iCP),int(nonCP),r3(offsetPop+nontractTWP),r3(nontractTWP),\"t,loop,R,iCP,nonCP,pop,target\")\n",
    "        if loopNo >= maxLoopNo-1:\n",
    "            print(\"WARNING. Looped\",loopNo,\"times for t,angles,R\",t,r3(STARTANGLE),r3(ENDANGLE),r5(guessedR),\n",
    "                  \"wedgePop offset, target are\",int(offsetPop), int(nontractTWP) )    \n",
    "    finalPop = iCP + nonCP\n",
    "    finalList = iClist + nonClist\n",
    "    return finalPop, finalList, guessedR, loopNo\n",
    "\n",
    "#above = bisection.  Below solveWedgeB uses static unit-unit distances\n",
    "# Also tried scipy minimize, which doesn't seem any faster\n",
    "\n",
    "def solveWedgeB(nontractTWP,T, HC, POLLY, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that intersect,\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2x2 array\n",
    "    \"\"\"\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    \n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList = 0, 0, list()\n",
    "    notFull = True\n",
    "    while notFull :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist    \n",
    "\n",
    "def solveWedgeC(nontractTWP,T, HC, POLLY, STARTANGL, ENDANGL, TOLERPOP, TRACTCP, TRACTPOP, COUNTYNO,\n",
    "               COUNTYTRACTLIST, COUNTYGEOM, NEIGHBORCOUNTYLIST, UUDIST, UUANGLE):\n",
    "    \"\"\"\n",
    "    This heart of the code solves the wedge radius that gives the closest wedgePop to the TargetWedgePop\n",
    "    We first determine which counties intersect the maxWedgePop to reduce the candidate list\n",
    "    We then go through the tract list from closest to farthest, adding any (from candidate counties) that\n",
    "    fall within the angle range (in lieu of wedgeB's check for intersection),\n",
    "     until the target pop is reached. Note that the passed UUDIST is the slice for unit t, not the full 2D array\n",
    "    \"\"\"\n",
    "    pi = 3.141592653\n",
    "    popTol = TOLERPOP  #default tolerance = e.g. within 0.7 an AVERAGE tract's population.\n",
    "    STARTANGL, ENDANGL = STARTANGL % (2.*pi), ENDANGL % (2.*pi)\n",
    "    #preliminary - restrict the found units to those in contiguous counties to the home county\n",
    "    checkedClist = [HC]\n",
    "    intersectedClist = [HC]\n",
    "    latestClist = [HC]\n",
    "    while len(latestClist) > 0:\n",
    "        newClist = list()    #we loop until we don't find any more neighbors with intersection\n",
    "        for c in latestClist:\n",
    "            for cc in NEIGHBORCOUNTYLIST[c]:\n",
    "                if cc not in checkedClist:\n",
    "                    checkedClist.append(cc)\n",
    "                    if POLLY.intersects(COUNTYGEOM[cc]):\n",
    "                        intersectedClist.append(cc)\n",
    "                        newClist.append(cc)\n",
    "        latestClist = newClist.copy()\n",
    "    \n",
    "    idx = np.argsort(UUDIST)  #sort order from closest to farthest to the home unit\n",
    "    \n",
    "    i, capturedPop, capturedList, latestDist = 0, 0, list(), 0.00001  #dummy value\n",
    "    notFull = True\n",
    "    while notFull and i < len(UUDIST) - 2 :  #capturedPop < nontractTWP - popTol : \n",
    "        i +=1    #this skips over the home tract\n",
    "        TT = idx[i]\n",
    "        if COUNTYNO[TT] in intersectedClist and TT != T:  #just to be sure\n",
    "            if isIncludedA(STARTANGL, ENDANGL, UUANGLE[TT]) : #POLLY.contains(TRACTCP[TT]):\n",
    "                capturedPop += TRACTPOP[TT]\n",
    "                capturedList.append(TT)\n",
    "                latestDist = UUDIST[TT]\n",
    "            if capturedPop > nontractTWP - popTol:\n",
    "                notFull = False\n",
    "                \n",
    "    return capturedPop, capturedList, latestDist \n",
    "\n",
    "\n",
    "def getPartialTract(t, HDTRACTLIST, offsetPop, UUDIST, tractPop, neighborLIST):\n",
    "    \"\"\"\n",
    "    If offsetPop is >0, this method identifies the tract with centroid farthest from Home t that can jettison at least offsetPop ...\n",
    "    ... by slicing out part of this tract.\n",
    "    If offsetPop <0, we ID a tract CLOSEST to Home t among neighbors of in-HD tracts to pick up a partial\n",
    "    We return the tractID and its new partial use\n",
    "    We have updated this method to pass the uuDist slice for tract t instead of computing tract distances inside here\n",
    "    \"\"\"\n",
    "    debugGPT = False\n",
    "    NTRACTS = len(tractCP)\n",
    "    bigDist = 9999999.\n",
    "    qualifyingTractList = list()\n",
    "    isWholeTract = False\n",
    "    if offsetPop > 0:\n",
    "        homeDist = [0.]*NTRACTS  #default = 0 to not get picked\n",
    "        for TT in HDTRACTLIST:\n",
    "            if tractPop[TT] > offsetPop:\n",
    "                qualifyingTractList.append(TT)\n",
    "        for TT in qualifyingTractList:\n",
    "            homeDist[TT] = UUDIST[TT]\n",
    "        if len(qualifyingTractList) == 0:  #very rare case where all in-HD tracts are too small.  Jettison nearly the whole farthest one\n",
    "            isWholeTract = True\n",
    "            for TT in HDTRACTLIST:\n",
    "                if tractPop[TT] > 0.9*np.average(tractPop): #set arbitrary min qualifying pop\n",
    "                    homeDist[TT] = UUDIST[TT]\n",
    "        targetT = homeDist.index(np.max(homeDist))\n",
    "        partialUseFrac = max(0.0001,(tractPop[targetT] - offsetPop)/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(\"positive offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    else: #pick up a partial\n",
    "        homeDist = [bigDist]*NTRACTS\n",
    "        for TT in HDTRACTLIST:\n",
    "            for TTT in neighborList[TT]:\n",
    "                if TTT not in qualifyingTractList and TTT not in HDTRACTLIST and tractPop[TTT] > abs(offsetPop):\n",
    "                    qualifyingTractList.append(TTT)\n",
    "        for TTT in qualifyingTractList:\n",
    "            homeDist[TTT] = UUDIST[TTT]\n",
    "        if len(qualifyingTractList) == 0 :  #rare case where most nearby tracts are small.  Add nearly the whole biggest one\n",
    "            isWholeTract = True\n",
    "            maxPop = 0.\n",
    "            targetT = -999\n",
    "            for TT in HDTRACTLIST:\n",
    "                for TTT in neighborList[TT]:\n",
    "                    if TTT not in qualifyingTractList and TTT not in HDTRACTLIST : # we'll take just one, even if doesn't fill gap\n",
    "                        qualifyingTractList.append(TTT)\n",
    "            for TTT in qualifyingTractList:\n",
    "                if tractPop[TTT] > maxPop:\n",
    "                    targetT = TTT\n",
    "                    maxPop = tractPop[targetT]\n",
    "        else:\n",
    "            targetT = homeDist.index(np.min(homeDist))\n",
    "        partialUseFrac = min(0.9999, -1.* offsetPop/tractPop[targetT] )\n",
    "        if debugGPT:\n",
    "            print(t,\"'s negative offsetPop =\",offsetPop,\", ID'd tract\",targetT,\"with pop\",tractPop[targetT] )\n",
    "    \n",
    "    return targetT, partialUseFrac\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=4):\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 6, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d041d328-83e0-45db-a869-e0956c23ee26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the state postal code; e.g. OH  NY\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, enter 1 below to use ny_pl2020_vtd.dbf as this state's population data file\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attempting to read in state unit geometries.  Stand by ...\n",
      "I read in the Census popn data. NY has 20201249 peeps and is divided into 14191 shapes.\n"
     ]
    }
   ],
   "source": [
    "STATE = str(input(\"Enter the state postal code; e.g. OH \")).upper()\n",
    "popFilename = STATE.lower()+\"_pl2020_vtd.dbf\"\n",
    "print(\"OK, enter 1 below to use\",popFilename,\"as this state's population data file\")\n",
    "enter1 = input(\" \")\n",
    "if int(enter1) != 1:\n",
    "    popFilename = input(\"OK, then enter the pop data file.  Typically a xx_pl2020_vtd.dbf\")\n",
    "print(\"attempting to read in state unit geometries.  Stand by ...\")\n",
    "tractPopFile = gpd.read_file(\"state_map_files/\"+popFilename)\n",
    "trueTractGeom = tractPopFile['geometry']\n",
    "nTracts = len(trueTractGeom)\n",
    "tractPop = tractPopFile['P0010001']\n",
    "statePop = np.sum(tractPop)\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "print(\"I read in the Census popn data.\",STATE,\"has\",statePop,\"peeps and is divided into\",nTracts,\"shapes.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bfdea9d-b0ea-497a-a556-6b488a3e333e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the number of districts in the state.  My guess is 27 26\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 62 counties in NY .  I will assign them county Nos 0 - 61\n"
     ]
    }
   ],
   "source": [
    "tractGeom = trueTractGeom.copy()   #for some states, we will modify geom, so preserve original\n",
    "#tractNAME20 = tractPopFile['NAME20']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "inputfileCountyNo = tractPopFile['COUNTYFP20']\n",
    "vtdGEOID20 =  tractPopFile['GEOID20'] \n",
    "nDistricts = int(round(statePop/760000,0))\n",
    "nCutDistricts = 0\n",
    "nDistricts = int(input(\"Enter the number of districts in the state.  My guess is \"+str(nDistricts)))\n",
    "tractArea = [tractGeom[t].area for t in range(nTracts)]\n",
    "trueTractCP =   [tractGeom[t].centroid for t in range(nTracts)]\n",
    "tractCP = trueTractCP.copy()  #for some states, we move secluded corners into the map, so save the true data\n",
    "tractCPx =  [tractCP[t].x for t in range(nTracts)]\n",
    "tractCPy =  [tractCP[t].y for t in range(nTracts)]\n",
    "inputCountyNumbers = list()\n",
    "for t in range(nTracts):\n",
    "    if inputfileCountyNo[t] not in inputCountyNumbers:\n",
    "        inputCountyNumbers.append(inputfileCountyNo[t])\n",
    "nCounties = len(inputCountyNumbers)\n",
    "print(\"There appear to be\",nCounties,\"counties in\",STATE,\".  I will assign them county Nos 0 -\",int(nCounties-1))\n",
    "sortedICNs = list(np.sort(inputCountyNumbers))\n",
    "countyNo = [sortedICNs.index(inputfileCountyNo[t]) for t in range(nTracts) ]\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adffc7a4-a78c-477f-8ea4-7f8e5109fa2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now build the county-by-county geometries, hoping there are no islands\n",
      "Building county geom for county 0\n",
      "Building county geom for county 20\n",
      "Building county geom for county 40\n",
      "Building county geom for county 60\n",
      "Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop < 4.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There appear to be 27 unpopulated coastal tracts.  Lets plot them and their parent counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"We will now build the county-by-county geometries, hoping there are no islands\")\n",
    "skipList = list()  #a list of units we previously know to skip in the map\n",
    "isAlteredCounty = [False for c in range(nCounties)]\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "countyPop =       [0. for c in range(nCounties)]\n",
    "countyGeom =      [dummyPoly for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    if t not in skipList:\n",
    "        c = countyNo[t]\n",
    "        countyTractList[c].append(t)\n",
    "        countyPop[c] += tractPop[t]\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Building county geom for county\",c)\n",
    "    for t in countyTractList[c]:\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            countyGeom[c] = tractGeom[t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "origMAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    origMAP = origMAP.union(countyGeom[c])\n",
    "    if countyGeom[c] == dummyPoly:\n",
    "        print(\"WARNING! county\",c,\"is empty!\")\n",
    "    else:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "minTractPop = 4.5\n",
    "print(\"Here is your original county-based map b4 any triage; e.g eliminating coastal unpopulated tracts w pop <\",minTractPop)\n",
    "plt.show()\n",
    "if origMAP.geom_type == dummyPoly.geom_type:\n",
    "    mapExterior = origMAP.exterior\n",
    "else:\n",
    "    for i,geo in enumerate(origMAP.geoms):\n",
    "        if i == 0:\n",
    "            mapExterior = geo.exterior\n",
    "        else:\n",
    "            mapExterior = mapExterior.union(geo.exterior)\n",
    "\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        if tractPop[t] < minTractPop:\n",
    "            if tractGeom[t].intersects(mapExterior):\n",
    "                isAlteredCounty[c] = True\n",
    "                skipList.append(t)\n",
    "print(\"There appear to be\",len(skipList),\"unpopulated coastal tracts.  Lets plot them and their parent counties\")\n",
    "for t in skipList:\n",
    "    plotPoly(tractGeom[t])\n",
    "    plotCenter(t,tractGeom[t],6)\n",
    "plotPoly(origMAP,0.2)\n",
    "for c in range(nCounties):\n",
    "    if isAlteredCounty[c]:\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "trueMAP = origMAP  #use these terms interchangeably"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a6c93c4-af08-47d5-90f7-dc0b4513aad5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last chance to alter the skipList, otherwise the above 27 units will be axed\n",
      "here is the proposed skipList [7183, 12318, 12569, 13043, 5399, 5400, 4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004, 11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]\n",
      "here is the final skipList [7183, 12318, 12569, 13043, 4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004, 11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]\n"
     ]
    }
   ],
   "source": [
    "print(\"Last chance to alter the skipList, otherwise the above\",len(skipList),\"units will be axed\")\n",
    "print(\"here is the proposed skipList\", skipList)  #NY - drop 5399, 5400\n",
    "skipList = [7183, 12318, 12569, 13043,  4021, 10232, 10308, 10364, 10429, 10551, 10563, 10707, 10799, 10872, 11004,\n",
    "            11133, 8290, 8291, 5209, 6535, 6559, 6600, 6699, 6788, 5825]\n",
    "print(\"here is the final skipList\", skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2989f381-466b-4882-bf1d-cfa37a7ba8ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now preserve the original county unit lists and geoms, then rebuild as needed\n",
      "removing coastal units from countyNo 2\n",
      "removing coastal units from countyNo 23\n",
      "removing coastal units from countyNo 27\n",
      "removing coastal units from countyNo 29\n",
      "removing coastal units from countyNo 30\n",
      "removing coastal units from countyNo 40\n",
      "removing coastal units from countyNo 42\n",
      "removing coastal units from countyNo 51\n",
      "Here is the revised state county map after eliminating coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Excellent!  We have no populated islands.  SKIP the below code block.\n",
      "we will scale longitudinal distance by 0.73321\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now preserve the original county unit lists and geoms, then rebuild as needed\")\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "trueCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    trueCountyTractList[c] = countyTractList[c].copy()\n",
    "    if isAlteredCounty[c]:\n",
    "        print(\"removing coastal units from countyNo\",c)\n",
    "        countyTractList[c] = list()\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in trueCountyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                countyTractList[c].append(t)\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "        if countyGeom[c] == dummyPoly:\n",
    "            print(\"Uh oh! county\",c,\"didn't have any usable tracts\")\n",
    "MAP = countyGeom[0]\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c])\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "origPopMAP = MAP   #origPopMAP is the map after eliminating coastal unpopulated tracts, with original islands\n",
    "print(\"Here is the revised state county map after eliminating coastal tracts\")\n",
    "plt.show()\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    print(\"Excellent!  We have no populated islands.  SKIP the below code block.\")\n",
    "else:\n",
    "    print(\"We appear to have some populated islands.  Separate out the main state geom in next block.\")\n",
    "LAT = MAP.centroid.y\n",
    "xScale = (1. - 1.089* abs(LAT/90)**1.9)\n",
    "print(\"we will scale longitudinal distance by\",r5(xScale))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a562a21-4107-4a3c-a320-90442a473387",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\n",
      "unit 286 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 292 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 293 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 294 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 3112 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit 1771 has island pieces.  We will reduce the tract to its main state component\n",
      "unit 1772 has island pieces.  We will reduce the tract to its main state component\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RUN THIS ONLY IF WE FOUND POPULATED ISLANDS\n",
    "nSubGeoms = len(MAP.geoms)\n",
    "subGeoms = [MAP.geoms[n] for n in range(nSubGeoms)]\n",
    "subAreas = [subGeoms[n].area for n in range(nSubGeoms)]\n",
    "mainSubGeomNo = subAreas.index(np.max(subAreas))\n",
    "mainGeom = subGeoms[mainSubGeomNo]\n",
    "nonMainGeom = dummyPoly\n",
    "for geo in subGeoms:  #the \"nonMainGeom is all pieces of the state not in the biggest piece\n",
    "    if geo != mainGeom:\n",
    "        if nonMainGeom == dummyPoly:\n",
    "            nonMainGeom = geo\n",
    "        else:\n",
    "            nonMainGeom = nonMainGeom.union(geo)\n",
    "        \n",
    "print(\"Lets first identify which county each island belongs to.  Then we'll move the island to the closest county mainland\")\n",
    "hasIsland, isIsland = [False for c in range(nCounties)], [False for c in range(nCounties)]\n",
    "isIsland =  [False for t in range(nTracts)]\n",
    "islandXshift, islandYshift = [0. for t in range(nTracts)], [0. for t in range(nTracts)]\n",
    "islandList = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(nonMainGeom):  #this county contains an island.  Find all its island tracts\n",
    "        hasIsland[c] = True\n",
    "        if countyGeom[c].disjoint(mainGeom):  #need to connect the whole county\n",
    "            print(\"county\",c,\"is completely disconnected from mainland.  Move it to adjoin mainland\")\n",
    "            isIsland[c] = True\n",
    "            countyDist = [999999. for cc in range(nCounties)]  #default, to avoid picking island counties as closest to this one\n",
    "            for cc in range(nCounties):\n",
    "                if countyGeom[cc].intersects(mainGeom):\n",
    "                    countyDist[cc] = countyGeom[c].distance(countyGeom[cc].intersection(mainGeom) )\n",
    "            closestCounty = countyDist.index(np.min(countyDist))\n",
    "            p1, p2 = nearest_points(countyGeom[closestCounty],countyGeom[c])\n",
    "            for t in countyTractList[c]:\n",
    "                islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "            for t in countyTractList[c]:\n",
    "                plotPoly(tractGeom[t])\n",
    "                plotCenter(t,tractGeom[t])\n",
    "                plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                tractCP[t] = tractGeom[t].centroid\n",
    "                tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                plotPoly(tractGeom[t],0.2)\n",
    "        else: #move only the county's island tracts to connect with main geom\n",
    "            mainCountyGeom = countyGeom[c].intersection(mainGeom)\n",
    "            plotPoly(mainCountyGeom)\n",
    "            plotCenter(c,mainCountyGeom)\n",
    "            for t in countyTractList[c]:\n",
    "                if tractGeom[t].intersects(nonMainGeom) and t not in skipList:\n",
    "                    if tractGeom[t].intersects(mainCountyGeom):\n",
    "                        print(\"unit\",t,\"has island pieces.  We will reduce the tract to its main state component\")\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "                        tractGeom[t] = tractGeom[t].intersection(mainCountyGeom)\n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                    else:    \n",
    "                        isIsland[t] = True\n",
    "                        print(\"unit\",t,\"is a true island.  We will move it to adjoin the mainland in same county.\")\n",
    "                        islandList.append(t)\n",
    "                        if tractGeom[t].geom_type != dummyPoly.geom_type :  #island tract is multiple geoms.  collapse to its largest isle\n",
    "                            isleGeos = [geo for geo in tractGeom[t].geoms]\n",
    "                            isleAreas = [geo.area for geo in isleGeos]\n",
    "                            biggestIsleNo = isleAreas.index(np.max(isleAreas))\n",
    "                            tractGeom[t] = isleGeos[biggestIsleNo]\n",
    "                            tractCP[t] = tractGeom[t].centroid\n",
    "                        p1, p2 = nearest_points(mainCountyGeom, tractGeom[t])\n",
    "                        islandXshift[t], islandYshift[t]  = p1.x - p2.x , p1.y - p2.y\n",
    "                        plotPoly(tractGeom[t])\n",
    "                        plotCenter(t,tractGeom[t])\n",
    "                        plt.arrow(tractCP[t].x, tractCP[t].y,islandXshift[t], islandYshift[t])\n",
    "                        tractGeom[t] = translate(tractGeom[t], xoff=islandXshift[t], yoff=islandYshift[t])                   \n",
    "                        tractCP[t] = tractGeom[t].centroid\n",
    "                        tractCPx[t], tractCPy[t] = tractCP[t].x, tractCP[t].y\n",
    "                        plotPoly(tractGeom[t],0.2)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a94118d-5863-4458-b77a-67d18cf01c2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\n",
      "This would need adjustment for multi-county islands like Michigan UP\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with island triage - RUN ONLY WITH ISLANDS\n",
    "print(\"If you like my proposed translations, let's rebuild the MAP with the adjusted county geoms\")\n",
    "print(\"This would need adjustment for multi-county islands like Michigan UP\")\n",
    "for c in range(nCounties):\n",
    "    if hasIsland[c] :  #rebuild the county geom with the translated islands\n",
    "        countyGeom[c] = dummyPoly\n",
    "        for t in countyTractList[c]:\n",
    "            if t not in skipList:\n",
    "                if countyGeom[c] == dummyPoly:\n",
    "                    countyGeom[c] = tractGeom[t]\n",
    "                else:\n",
    "                    countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "MAP = countyGeom[0]\n",
    "plotPoly(countyGeom[0])\n",
    "for c in range(1,nCounties):\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aebe4ee-ee7b-4bf3-8413-f315556d453e",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing all county centerpoints, then plot them\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"computing all county centerpoints, then plot them\")\n",
    "countyCP  = list()\n",
    "cutCountyList = list()\n",
    "for c in range(nCounties):\n",
    "    cTL = countyTractList[c]\n",
    "    countyCP.append(getHDcp( [tractCP[v] for v in cTL], [tractPop[v] for v in cTL], [i for i in range(len(cTL) ) ] ) )\n",
    "    if countyCP[c].disjoint(countyGeom[c]):  #possible with weird-shaped county\n",
    "        countyCP[c] = nearest_points(countyGeom[c],countyCP[c])[0]\n",
    "    plotPoly(countyCP[c].buffer(0.03))\n",
    "    plotPoly(countyGeom[c],0.5)\n",
    "plotPoly(MAP)\n",
    "plt.show()   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c47f795-8f36-40d7-aa39-19cdcdc5d4d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE = \"NYupper\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "207f3a55-e361-4777-ae46-41fe3d54a4b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block reads in a csv with whole districts to be cut out of the map\n",
      "For NYupper my input file says to cut out 1 districts out of 26\n",
      "performing cut no 0 for state NYupper\n",
      "the total proposed cutPop is 12426079.0 . Compare to 776971 15.993\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to apply the cut 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your cut districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"This code block reads in a csv with whole districts to be cut out of the map\")\n",
    "unclippedMAP = MAP\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = trimDF[\"nClipPoly\"][DFrow]\n",
    "nCuts  = int(trimDF[\"nCutPoly\"][DFrow])\n",
    "nCutDistricts = nCuts\n",
    "nStops = trimDF[\"nStopLines\"][DFrow]\n",
    "nDistricts = trimDF[\"nDistricts\"][DFrow]\n",
    "stopLines = list()  #the 'stoplines' stop wedge growth that hops across concave map areas (not currently used)\n",
    "cutCountyList = list()\n",
    "allCutLists, allCutPops = list(), list()\n",
    "print(\"For\",STATE,\"my input file says to cut out\",nCuts,\"districts out of\",nDistricts)\n",
    "if nCuts > 0:\n",
    "    cutList = list()\n",
    "    cutXs = ast.literal_eval(trimDF[\"cutXs\"][DFrow])\n",
    "    cutYs = ast.literal_eval(trimDF[\"cutYs\"][DFrow])\n",
    "    cutPoints = [Point(cutXs[i],cutYs[i]) for i in range(len(cutXs)) ]\n",
    "    for cutNo in range(nCuts):\n",
    "        print(\"performing cut no\",cutNo,\"for state\",STATE)  #first two cutPoints must form the cutLine\n",
    "        cutNotDone = True\n",
    "        thisCutPoints = [ cutPoints[int(4*cutNo)],cutPoints[int(4*cutNo+1)],cutPoints[int(4*cutNo+2)],cutPoints[int(4*cutNo+3)]  ]\n",
    "        while cutNotDone:\n",
    "            cutPoly = Polygon([thisCutPoints[0],thisCutPoints[1],thisCutPoints[2],thisCutPoints[3] ])\n",
    "            cutLine = LineString([thisCutPoints[0], thisCutPoints[1] ] )\n",
    "            cutList = list()\n",
    "            cutPop = 0.\n",
    "            for c in range(nCounties):\n",
    "                if cutPoly.intersects(countyGeom[c]):\n",
    "                    if cutPoly.contains(countyGeom[c]):\n",
    "                        cutList = cutList + countyTractList[c]\n",
    "                        cutPop += countyPop[c]\n",
    "                    else:\n",
    "                        for t in countyTractList[c]:\n",
    "                            if cutPoly.contains(tractCP[t]):\n",
    "                                cutList.append(t)\n",
    "                                cutPop += tractPop[t]\n",
    "                            if STATE == \"NC\":\n",
    "                                if t == 1828:\n",
    "                                    print(\"special for NC - include vtd 1828 from county 55 in the cut list for contiguity\")\n",
    "                                    cutList.append(t)\n",
    "                                    cutPop += tractPop[t]\n",
    "            print(\"the total proposed cutPop is\",cutPop,\". Compare to\",int(statePop/nDistricts),\n",
    "                  \"avg districtPop.  This is\",r3(cutPop*nDistricts/float(statePop)),\"*aDP\" )\n",
    "\n",
    "            doIcut = input(\"enter 1 to apply the cut\")\n",
    "            if str(doIcut) == str(1):\n",
    "                cutNotDone = False\n",
    "                allCutLists.append(cutList)\n",
    "                allCutPops.append(cutPop)\n",
    "            else:\n",
    "                print(\"Here is the state and the last proposed cutPoly.\")\n",
    "                plotPoly(cutPoly)\n",
    "                plotPoly(MAP)\n",
    "                plt.show()\n",
    "                print(cutPoly)\n",
    "                oldPts = list(cutPoly.exterior.coords)\n",
    "                newPtXs = ast.literal_eval(input(\"enter alternate [ x0, x1 ] for the first two points\") )\n",
    "                newPtYs = ast.literal_eval(input(\"enter alternate [ y0, y1 ] for the first two points\") )\n",
    "                thisCutPoints = [Point(newPtXs[0],newPtYs[0]), Point(newPtXs[1],newPtYs[1]),oldPts[2], oldPts[3] ]\n",
    "allCutVTDs = list()\n",
    "for L in allCutLists:\n",
    "    for v in L:\n",
    "        allCutVTDs.append(v)\n",
    "if nCutDistricts == 0:\n",
    "    print(\"There were no cut districts in\",STATE)\n",
    "else:\n",
    "    print(\"here are your cut districts\")\n",
    "    plotPoly(MAP)\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        cutGeo = tractGeom[L[0]]\n",
    "        for v in L:\n",
    "            cutGeo=cutGeo.union(tractGeom[v])\n",
    "        plotPoly(cutGeo,2)\n",
    "        plotCenter(i,cutGeo)\n",
    "        plotCenter(allCutPops[i],Point(cutGeo.centroid.x,cutGeo.centroid.y-0.5) )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4c915415-6c44-4c43-9826-b565cebed95a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Write the vtd cutlists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Write the vtd cutlists to a file\")  #SKIP FOR NY, AS WE WILL DO LOWER SEPARATELY\n",
    "cutDistrictNo = list()\n",
    "for v in allCutVTDs:\n",
    "    for i,L in enumerate(allCutLists):\n",
    "        if v in L:\n",
    "            cutDistrictNo.append(i)\n",
    "            break\n",
    "nbrDF = pd.DataFrame( {\"vtdNo\":allCutVTDs,\"cutDistrictNo\":cutDistrictNo} )\n",
    "outname = STATE+\"wholeDistrictCuts.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "nbrDF.to_csv(outpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17721728-e5c0-4787-b430-9d7d94970667",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26 districts\n"
     ]
    }
   ],
   "source": [
    "countyPop = [0. for c in range(nCounties)]\n",
    "for t in range(nTracts):\n",
    "    countyPop[countyNo[t]] += tractPop[t]\n",
    "print(nDistricts,\"districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74686069-ffb9-47a0-921d-ef1b75e5e7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "nDistricts = 26  #special for upper NY\n",
    "nCutDistricts = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3073320-8d92-45ba-beb2-fb8c08d7170d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed all whole districts.  Finalize stats before squishing in outcroppings.\n",
      "Of the total statePop 20201249.0 we ignore 12426079.0 as it is cut out of the map\n",
      "This excluded pop comes from a total of 8144 tracts\n",
      "Our final adjusted number of state districts, excluded fixed cut-out is 10 rather than original 26\n",
      "Each district will be drawn to house 777517.0 = 1/ 10 of non-cutout state pop 7775170.0 vs original= 20201249.0\n",
      "Here is a county-level modified map with each county's fraction of a district pop\n",
      "working on county 0\n",
      "working on county 20\n",
      "working on county 40\n",
      "working on county 60\n",
      "here is the NYupper map excluding cut and unpop coastal tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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LF1asWEF0dPQ17fENdu7PvOC+9F+Hw0LiqT/ReSr1Pf4JKOJDQUGhXuJwWNi9cxJp57fRpfcHBAb2c7dJNYaHVzSiSiY343KKa3klzZOTk7Hb7bRp0waVSsWdd97JkiVLAPjmm2+YMmUKABqNBl9fXwBat25No0blp6wGhDQDIPtC3RQbKwubLQuHTcTgef1kLilUHUV8KCgo1DscDjO7dzxIevJuOvf+kICA3u42qUZxFRpLP+LaVl5J86vtz8nJQa1WM23aNDp06MB9991Hfn5+pezxC44BQSYn/XxVL6napKX/hSBCm9afuM0GhbpDER8KCgr1CofDxM7t/yLjwn669P6YAP8e7japxvFv0BKAvJzqexrsdjunTp1i8ODB7N69m9DQUN58881KzaFSadF6yuRnuC/mI+ncUrQGo5Jm+w9BER8KCgr1Bos1g62bxpGTfoSuvT/B3y/e3SbVCh7eYThsIqZihcbKK2l+tf0BAQF4e3u7inmNHDmSvXv3Vtomvbeagqycyl9MDWHKyyY0sle97cmjULMo4kNBQaFeUFh4is3rRlNUmEG3vl/j59fZ3SbVGqIo4igyYrZerixaXknzsLAwVCoV+/fvx+FwsHDhQoYPH44gCNxyyy1s2bIFgHXr1tGiRYtK22T0M1KYXVj9i6sifkFNuJC4HVl2b7qvQt2giA8FBQW3k52zg01r70Sl0dGj72K8vOJq5TzlpbJOnjyZ4OBgOnXqVOb4UaNGXXVfpXF4Y5cuFxqrSEnzefPmMW7cOJo1a8agQYNo3bo14CxpPm3aNNq0acPff//Ns88+C8D8+fOJiIjg/PnzxMbGuoJSy8LTzxdznu2q+2ub4LB+WExZmM3J5R+scN0jyJfaD9YT8vLy8PHxITc3F29vb3ebo6CgUMtkZKxl+4Yn8A+Oo+NN89Foaufv3m6307JlS9auXYuPjw8dO3Zk8+bNBAQEuI7ZtGkTer2ehx9+mJ07d5YYv3LlShYsWMCpU6dK7asKK38eiUN1hkG37a32XDXBmoUvsueX3dw0tiu9bn++zs+fnrGa7esfo8eA7/DxaV/n51eoPpW5fyueDwUFBbeRkbmO7X8/QWhkVzp3/aLWhAeUn8oK0L179xJi5BI2m4033niD55+vuZuyThuKqDdRX57/mrYfAMD+ZZvdcv4A/154+IRyYM8L2O2Vy9ZRuP5QxIeCgoJbyMz8mx1/P05wVBfadpiHKOpq9XzlpbJei/fee497770XLy+vGrPHaIxEpXFgLsos/+A6ICq2F+1HtsNaIOBw2OvsvA6HRG5GFonHj2MUHuD8fj8Wz5tCXo57q60q1C5KV1sFBYU6Jy1tOTs3/psGEZ1o32Eeoqh1t0lXJSkpiRUrVrBq1SrOnj1b/oAK4unbmNwcyE47gqFhzxqbtzr4hUQiS3vJST1FQFhsrZ3HYi7gh7f+pCDNB8mh5vJzcAhwH5Iji13LztB3XHCt2aDgXhTxoaCgUKckJ//A7s2vENGkL23avldnwqOsVNXOncvPqNm7dy+HDx+mUaNG2O120tPTGTJkSIn28lXBNyCWpBzIzTpBWD0RH/6hTQHISD5au+KjyEReShAqbQFtBmbj6euNd0AAvgHB+ASEsmNJIruX7aVd39b4hXjUmh0K7kMRHwoKCnVGdvY29mx5hcbNb6NF3KsIgqrOzl08ldXHx4dly5bxwgsvlDtu6NChpKSkAJCQkMCoUaOqLTwA/INjkI9DQV5CteeqKYLCnMXPslLPlHNk1ZEkid8+WAZEEj/ak7Y9by11TPubG7Jz6T7OHspUxMcNihLzoaCgUCdYrZns3jqVgNA2dS48oGKprBMnTiQ+Pp79+/cTERHB4sWLa80erd4Du1lLkcl9Jc2vxOAZgtrgICel5m1KOLKXLX8u4c/PPicvOZLItmm07Vl2vx69p4bmXWPZ9utuzAXuS/9VqD2UVFsFBYVaR5Yldu24n+z0I/Qa8Ds6nbKWD7D0p5vQqhsw4LY/3W2Ki3fHDgNg6qIlNTJfetJpln2yg/zUINe2kNgM7nhqzDXH5WeZ+WLGnwyd3IvG7YKueaxC/aAy929l2UVBQaHWSUn5iQvnthPf9yNFeBRDlH1wyNnuNqMUKm31S5znZl7gzKEDHFh3gcIsf9oPz8E/JJSiAhOxHYaWO97TT4fWoCIzqUARHzcgivhQUFCodVLOryAguM0N1522umhUgdjVB91tRgkiOviTm5JVpbEZyals/GUNGQlarCYPZIcWldaXHuMNtI4vX3AURxAEvPwNFGSZq2SLQv1GER8KCgq1iiw7yLiwh5jW97rblHqHTheGrNmJLEsIQv0IwTN6+5F2Mg1JkshJy8RUmEpeZgb52bnkZxdSlGejKF/GXKDCWqjDkhcIgE94CvmpfohqPV7BuQQ1MdGmZ0uiW/RBFKt2bUZfL7KS60cdFIWaRREfCgoKtUrKhV+w2wrx8+vqblPqHR6eUVjsMgW55/HyjarTc0uSRFZyBhdOJZJxLonslAvkZaSRk5qF3aLno0fXXTHCC1GjQWMwofOwYvBy4B9mw5Sbhs0sI6olItvm0Ht0P7x8Q2rERi9/H1KOn6uRuRTqF4r4UFBQqDWSkxeze/NLRDcbiq9PDTVku4Hw9mtKVjpkpR2uFfEhSRLHtx8k7fR5spJTyM9IpTAnA0thFnZrDlCskqmgQ6PzQ+8RgiGyCYGNcrEWehIb70VASAg+gSHoDPoat/Fq2G0OTu8+RfNuzersnAp1hyI+FBQUaoWsTc+xO/kX/IMCaNX6bQSh+kGMNxp+wS1ISHcWGoNBNT7/5h9Xsu2nD5zfCBrUWj/0ngH4R7bAJ6gB/mFhBEWFEdI0Ep8g3xo/f3U4tTsdc6GV1r3r1iOkUDco4kNBQaFmkRw4lj1D0dZv8etqoEhK5vBfPWna5VN0/i3dbV29wjegIZJdoDC/5sq2F8fg6SzQNeixmbTo3q7KsRfu4PiOJALCffENNrrbFIVa4Pr5TVRQUKj/ZJzA+tUYsjd+Q9jwl+k26CjNjbeRobrA1p3DSNzwAJK9yN1W1htElQp7kR5zUXKtzB/aNBqAgszs60p47FuTSMLek7S/Jc7dpijUEtfPb6OCgkL95uQqcv7Tn6ITmwi873OEzg8iqNSEd32P+K6raEBDjlvXsGNFO7KPfOZua+sPdm9s9rRambpBo1BATXpi/amiWhEO/X2Ypp2a06JbqLtNUagllGUXBQWFymMthD3fYjr4FzovXxz5aeSf3IVnk47oxn0BHoElDtd6N6LFLWsIP/Ujx469yO6UNwg++xlNO32MPrCNe66hnqAW/LELF2pnbo0atc6P7JTa8azUFlqDAYvJ5G4zFGoRxfOhoKBQcSQH8t+zyZ7VnqzfXkbEQf7Zg1hyMwkYMxvd/b+XEh7F8W4yik4D99PSOIJs4QJb9ozg9K/dcJjrX5XPukKjDUbUFNba/HrPQAoyU2tt/tqgRbcmJB5NVvq63MAong8FBYWKUZSDddG/yDu2Ff/u4xC7/x/4RVPZ5EtBpSa067sE5T1BwtrRJHheIHlNe5qGPkBw22cRrqPYhJrAaIzALluxWgrR6mq+g6tXYAipp/fU+Ly1iV+oB8hgyrOi99S42xyFWuCf9VeuoKBQeSQJErdT+N8BFJ3dS+C/vkIcNhv8oqs1rdo7iqa3baOr/zS8JR8OZX/OruVtyTvzSw0Zfn3g5dMIQYCs1KO1Mr9fSBiSLRebxVor89cGl9Ky61nfU4UaRBEfCgoKZZOdgPXr8WS91IisD29FUGnweWwFNO1fo6cxdniUNkP30j7g/7DLFnacnsrhv3phyaxfPU9qC59AZxGtnPTaER8BkeGATOqZpFqZvzbwbWAAIDOpwM2WKNQWivhQUFAoTcJGst/rhSVhB/4978H/X99ifGwNBDSpnfMJAv5tn6TzLfuI1Q8kXTzPll23cnb93UjW/No5Zz0hINhZ+yQ/90ytzB/cKAKAC6cTa2X+2sDgpcU/zIdTe66vQFmFiqPEfCgoKJTmzAbQGPGasQu0NR+HcDVEtYGI7h8RnHeW09se4qR9E8mrOxATPpnANk/WmR11iU7vi8OixmSvHXEQ0jgcEMhMvL5u5JEtoji954S7zVCoJRTPh4KCQmmsBaD3rlPhURyNdzSxNy+nS8x/0cl69mV8wL5lHTGlbHSLPbWNZDVitdVORorOqENUe5OTWjvpvLWBLMucPZBASGOlzseNiiI+FBQUSiLLyNlnQW1wtyV4Rg2m/aB9tPK5mwIhh62H7uHkmhHYTenuNq1GESQf7HLttY7XGvzJv07SbU15VhbO/JuctFxa9WnkbnMUaglFfCgoKFzGZsbx+xTS967BN36su60BQBBFgju+TNfe22gotCfRsZ8tG7qSsP1pJIfD3ebVCGoxAFR5tTa/wTsQc37tiZuaZO3X+8k4n8Ggh3sREevnbnMUaglFfCgo/FNx2CEv2flKPYS8/DmyZ7Ujd/sPNBg9CyH+UXdbWAKVIZDG/X+ia+tvURWKnCpYzA9r2nMw6U93m1ZttNoQVLra63njFdAAa1HWVfcvWbKE2NhYYmJiWLBgQan927dvJy4ujqZNmzJz5sxS+0eNGkWnTp1c3+/bt48uXbrQrl07unfvzunTpytk54XTuZzee4Z+93QlplNwhcYoXJ8o4kNB4Z+G5MCy8F9kvxJD9pvtna//9CNn62J8Oo3Af8rf0PFed1t5VdT+bZiTY2TxBT0CdlKOPs7Cv4eQkV872SJ1gdEYgUrnoKiwdrwT/qFhyJKZwtzS3hW73c6UKVNYs2YNe/bs4Z133iEzs6QdkydP5vvvv+fYsWMsXbqUAwcOuPatXLkSlUpV4vjnn3+emTNnsnfvXu6++27eeuutcm1MOpbN73P+xjvIV+np8g9AER8KCv80jvxO4b4/8ev7EH4Tv8Jv4tf4PbAIv+cOIA55s/bSaWuIz5c+xDHRwWMdXmVk313keA3Cw3qcbdtu5o9dT2Czm91tYqXx9GoIQHZa7dT6CIoOByDlxLlS+y55NcLDw/H09GTw4MGsWLHCtT85ORm73U6bNm1QqVTceeedLFmyBACbzcYbb7zB888/X2JOQRDIz3emSOfm5hIaWr6YWDp/M16BPox5tjeiSrk13egoP2EFhX8Sskzh6vcwNrkJ+j4DzQZCs1ugcR9Q69xtXbmcOLGUj3L2cZ93S1o1vx2t2sDozv+l003LyFA3QZ+zhN/WdWT76S/dbWqFcTisnD/3IwB52bXjvQltFgVAahm1PpKTkwkPD3d9Hx4eTlJSUoX2v/fee9x77714eXmVmPPtt99mypQpRERE8PnnnzNlypRr2pd+Lh9zvomOg5ph8NRW/gIVrjsU8aGg8E/i5GosKSfQ95vmbksqjd1WxPMbnyVaVvHo0P+V2BfqE8O9fZbj13Q2ZllNfsJMvlvbi7OZu91kbcXJzTgN3nux5vkQENKuVs4REBYIgpaM8zVX6yMpKYkVK1Zw772ll+g+/PBDPv74Y86fP8///d//lSs+kk/kIKpVNFXiPP4xKOJDQeEfRNHqd9FHtIRGvdxtSqX5YunDHBXsvNrlebQ6zzKP6RQ9kjv77cLkPx6D/QJH9o5m0eZx5Jsz6tjaiuPlF40sg7/XMILCWtXKOYoKTYiiJ7lppdNtw8LCSng6kpKSCAsLK3f/3r17OXz4MI0aNaJHjx4cOHCAIUOGALBw4ULX12PGjGHz5s3XtC/lVCZ+wV6IolCt61S4flAqnCoo/BOwFiKteYOChL0E3fspCNfXh/zJk8v5MHs3E72a07rl6Gseq1apGd7uVfLMj7N01//hW7SdNRu7oQkcxcA2r6AS61eXVI3WgMOswULVioDZrFYyEy+QdjaFzPMp5KamkZ+ZgSkvE0thDnZrLrLkjIOxFplKje/cuTMHDx4kKSkJHx8fli1bxgsvvODaHxYWhkqlYv/+/cTFxbFw4UI+/fRTWrduTUpKCgAJCQmMGjWKpUuXAuDv78/WrVvp2rUrq1evJjY29qr2p5/L58SO4/QY06VK169wfaKIDwWFG51jf5H/y9NYC7IJHDIVmg91t0WVwm4z88KGGUQg8uiw/5U/4CLe+iDu7L6QhMzdbNj/FGFZi/hxzR80ajydzo3vrkWLK49kM2CldOE0SXKQlpHJyfR0ErJzOZtfSJLZQopdJkNQkanRg8PBPT99iMZucw4S9Ki1PuiMvvgEN8YrIBCfBg3wDw8hpnNpz4parebdd9+lb9++SJLE9OnTCQgIYMiQISxYsICwsDDmzZvHuHHjMJvN3H333bRu3fqa1zN//nweeeQRJEnCx8eH//3v6j+3whwLoKJxu6BKvWcK1zeCXM96Fufl5eHj40Nubi7e3t7uNkdB4folLxnbH9PJO7gaz5gu6Ea8B/6N3W1Vpflq6UO8k76FL9vPoEObu6o8z/YzCzlx8g1CVIWkEkLrZm/ROqJHDVpaecyWXM4XnOXw1knkWw3sEqaQYnOQJotkanTk6D0w642XB8gyXhYTATYzQUioULHVy59ZyZn0jA2nQaMwvPy9XC3prwfOHsrk9znrGP/KQALCyl5OU7g+qMz9W/F8KCjciORfIH9OT2RRS8D4D6DVHRVaalmyZAlTp05FkiRmzJjBAw88UGL/5MmT+fHHH4mMjGTnzp2u7RMnTuTvv/92feD89NNPNGlSMym7BrURlSzz6q53eF6lpmPcnVWap3OjO+kYPZq/DryKNvU7Uo7dy+6Dwxkc/ywNfBrUiK3FkSSJNFMqSflnyShIJK/oPBZzMrI1GY39Ap5SGgacyyAeBthr6MnPtkD8VEUEyXbaqATCNXYiDXYa+frQNCiQKF9vdMVqapzNNNFl/3GEmBiadoqq8WuoC45uTsQnyBP/UPf0EVJwD4rnQ0HhBkRa+gw52xbjP30reARWaIzdbqdly5asXbsWHx8fOnbsyObNmwkICHAds2nTJvR6PQ8//HAp8TFq1CiGDRtW49cCcPToz7y6eSb7VQ7uMDbkhdt/QaWq+rPTsys+oqjgPwzwFHHIKsy6idza7TH02oqnGxdZLZxIPUOS5TxFthQKi85js6SgsiWjd6TiI6ejwXb5eAzkCg0wq4ORNKFodWEYDeH4GyMJ8YoiwiMEH23l0kwlSSJ69T7uEz2Y2b9ZpcbWB6xmO5899Ts33dqBToMbutschWqieD4UFP7JFKSRvfl7/Ps9WGHhASWLTQGuYlPjxo1zHdO9e3cSEhJq2uJyad78dr5uOpRHv+nBkoIzTDGl4+1V9SqYYd4RzE/RcFurzzh3+n+EOj7i91U/4RsyhQHtbkcURQrNJs6ln+ZC1hmy8s9hMiXhsKUgyqkYVel4a3JQiRJaQAuo8KZADMamCcas7wGGcLyNEQR6RBHhHUUDvT8qsWYTDEVRxN8ic06w1ui8dUXq6Tzsdpkm7ZV4j38a1RIfb775Js888wxPPPEEc+bMKbFPlmWGDBnCX3/9xS+//MKIESOqcyoFBYUKIm+aiyCqELo+Uqlx5RWbKo9p06bx3HPPMWTIEF577bVSJberS1r6YTYJZnqLXtUSHgANfUMAyLJJ3DPoYw4k7CTl4Kuocmbw41/voRWL8NJeLkXuLQsIki9FBOEQg7Fp2lFkDOeP0yKHUwysfnIUBo174hVCJJFk7G45d3XJSilEpRbxaWAs/2CFG4oqi48dO3Ywf/582rRpU+b+OXPmXFdBTwoKNwSFGWRv/Aa/3veB0b/OTjtr1ixCQkKwWCzce++9fPzxx0yePLlGzyGLTjETpa+4N+dqNPF3ipezOc5U0dYNOxEX9Qtr9/1K0YVV2NVBqAwR+HtHERrQiIiAaLSa0ksyS3MOknToLDqV+26eYSo1u+Xr0/NhNtkAO8kncpQOtv8wqiQ+CgoKmDBhAp9++imvvfZaqf179+7l3XffZefOnRWq6a+goFAzyJvnAQJC/GOVHltWManOnTtXaOylv3O9Xs8999zD4sWLK33+8sjIOAbA8Nb3V3uuJv4hyLJASsHl9FZRFOnf/nbg9grPE+VjQJDhTJ6ZJr7uESBRei0rZRuSQ7rueqK07RdJ0rFUfn13DaOeHkBIYx93m6RQR1TpN3Xy5MkMHTqUAQMGlNpnMpkYP348//3vfwkJCam2gQoKChXElEX2xq/w63kXeASUf/wVFC82VVBQwLJlyxg4cGCFxl4qNiVJEr///jtxcXGVPv/V+Hrpw/z7m17M3vEmAKKq+kXCtGo1guRJamHp2hqVoYm/U3Acziiotk1VtsHHgF0lcDardAGx+o7eQ8OIp7qg0as5d6h2Ovoq1E8qLT4WLlzI7t27mTVrVpn7n3rqKbp168Ztt91WofksFgt5eXklXgoKCpVH2vA+SA6Ebo9XaXzxYlPt2rVj6tSprmJTycnOniATJ04kPj6e/fv3ExER4fJwTJgwgTZt2tCmTRscDgePP141G8ri2wub2WPNwkvUMlIbTFREtxqZVyP7kGWpXtn12ABneuhJN974Yy4KoKNp7hNA1UEQBOxWB3qlodw/ikotuyQmJvLEE0+wcuVK9Hp9qf2///47a9asYc+ePRWec9asWbzyyiuVMUNBQeFK0o6Q/ffn+N/8f+BZ9cyBW2+9lVtvvbXEtkslswG++OKLMsetWbOmyucsjwiVHh+Vnnfv2lCj8+pVvuRZs6o1Rwt/T2QBzua4T3y0CPGCs3Ay+/rzfAAU5lqQHDLeAaXvKQo3LpXyfOzatYu0tDQ6dOiAWq1GrVazfv165s6di1qtZuXKlZw6dQpfX1/XfoA77riDPn36lDnnM888Q25uruuVmFi65bOCgsI1kCSKfn4KnX8YQo8n3W1NtTl1ajl3fdGRKV914/WFg0hzmEm05df4ebzU/pgc2dWaQ68WEWRYvb3iWUE1TU6eBbVd5lzR9Rl0KjnkEv8r/DOolOejf//+HDhwoMS2++67j+bNmzNjxgwCAwN5+OGHS+xv3bo177//PsOHDy9zTp1Oh05X8cI+CgoKV7DvO0xn9xHw0CJQX99/S6vWvcRzZ37EJIo0cjg4UJSPj6Cil2/zGj+Xny6AC9aDNTJXfoF7bvw/7kjkmfR0tCqBkbE1X6W1LvAONKD30JBxPp/GSr2PfwyVEh9eXl60alWyMZGHhwcBAQGu7WUFmUZFRdGoUaNqmKmgoFAmhZnk/DET7/bDoVEvd1tTZRyWAub9OpYF5nPcrGvAayN+xGisfNBsZQgyBnGwIA9JkhCrUfzLP8ITuY6f2q1WB1OWH+Eno41msshXXZoR7aZsm5pArRVxKJ6PfxRKhVMFhesY+18vgCyhGfK6u02pMrkX9jFj6US2iDaeCu3LfbfMrZMaQSEegQiinQuFOYR5Vb0mip+njsSUml8WuhpJmSbGbDzKaS+BuwUP3hjcFHUNV06tawRRqHMBp+Beqi0+1q1bd8399ax1jILCjUPCJnJ3/kLAHa9XK8jUnRzZOZ8p+/5Dvqjio07P0q3V+Do7d6S300t7MiO5WuIjyEvH6dPVix2pDNsTczjlLTLUrOHtwddfP5ey8PD1Iv18BtDU3aYo1BHXt1xWUPinYrdS+MtUjJFx0GGiu62pNLLdyuJfJjDh4Ad4ajxZOPyHOhUeAI38nOIjIedCteYJ9dEjWyUKrXVT4nxkuzAGm9Qs01r5dV9ynZyztmnWuRHnDydRmGNxtykKdYSy7KKgcB0ib/4Ac3oiAY//BfXY5W4yZbBs0yyMWi/8PEPx847AR+vDf9dM4VexiLG+rZg+7Eu0bgiUbRoQBkBiXmq15rkUa3Esu5AOwXVToXP+wBbcsnQ/U+wXaBpopFW4b52ct7aI7RzCjiV6Vn2xl9ue7OJucxTqgPr7qaWgoFA22Qlkr5yLX897IaS1u625Jut2zuPl5BVMT/iJBw/OY9Tmp7l53SMsE0y83uJ+nh+x0CU8lixZQmxsLDExMSxYsKDUXJe67jZt2pSZM2e6to8bN462bdvSqlUrHnnkESRJKjFu2rRpBAaW7gcT4uWNLGlJKUir1jU29TMAcCyzsFrzVAatWsXC3s0x2OGuPafJKri+PQZ6Tw19JtzEuUOJZCZdn8XSFCqH4vlQULiekGUsv/0bjdEbsd8z7ramXDJNaegliXWjVpGVk0B23jmyC1Jo1KgfUQ0uN6W02+1MmTKFtWvX4uPjQ8eOHRk5ciQBAZczXiZPnsz3339PXFwc3bt3Z+TIkbRu3Zr58+fj7e2NLMuMGTOG3377jZEjRwJw+PBhLlwoe1lFEAREyYcMU/WqnDYPcHazPV3HRb5CfAx8HteQ0cfPMn7NEZYMbYP6OuvtUhy19vq1XaHyKD9tBYXriaNLKDi2Aa8Rb4DOy93WlEt2URb+soCHVyiRkfG0iRtL7y5PlhAecNmrER4ejqenJ4MHD2bFihWu/cnJydjtdtq0aYNKpeLOO+9kyZIlAHh7ewPgcDiwWCwlMmWmT5/OG2+8cVX7dPiQba1eT5FG3npkERJziqo1T1Xo3NCfWQGB7PWUeWLF0To/f02Sm1YEyIgqpRv6PwFFfCgoXC9YCsj99Tm8m/eE5sPcbU2FyLLk4CeU72BNTk4mPDzc9X14eHiJDrvl7R81ahTBwcF4enq6ysMvWrSITp06ERUVddXzGtW+FNiqV2JdJYqo9GpS8szVmqeqjO8Yyf2SkZ/0Vj7cfAYAhyxzLC2fDzadYd2x6jXPqyusFjvIEvmZ7nkfFeoWRXwoKFwnONa8gWTKRXPrO1AHdTBqgmxbPn5i7QeT/vjjj6SkpCDLMqtXr6awsJC5c+cyY8aMa47z1gRQJOVU+/x2rcju9Lqr9XElr/WLoUeByBumHHr+sYfo1XvpfegUr1tzuTM5ie93nXebbRXB4ZDY/ttumnVtSWTLqqc9K1w/KDEfCgrXAxcOkrPhS/wHPQV+Dd1tTYXJcpiJ0pSfARIWFlbCk5GUlETnzp2vuT8sLKzEHFqtlpEjR/Lbb78REhLCyZMnadGiBQDZ2dm0adOG/fv3lxjjrw8gwZJbpWsrgV6FYJbKP66WEEWRL25pQZ/lBzjhJTKiQE0rHyNtQ7x48FQiT8npzPk9DYtKYLTKyL86RxLiX/cVUSWHRGpWEcczTJzONbE7JY+0s7kIBXayDAK6Q8foJ8WhUZZebngU8aGgUN+RJEw/P4UhMAKhW821qq8LMiQre+3phP86Hh+dL1EBzenZufQ1dO7cmYMHD5KUlISPjw/Lli3jhRdecO0PCwtDpVKxf/9+4uLiWLhwIZ9++ik2m43k5GSio6NxOBwsWbKEzp0707p1a1JTL6fQBgYGlhIeAMHGIMg3YbFb0FUj3bdlgAcnz9WAiKkGnjoNf/Zuyd7kPAa2uNznZXdjf+ZsTWCVlMdZL4EPKGLenmMsioygV7OaLU4nSzIZ2WaOpeVzMttEQr6ZRLONZMlOuiiRqRMo0l10uFsc6HalIthlGmk0WLw9SS9I5b9rTvLEgJg6qXKr4D4U8aGgUN/Z8zVFiQcJeHgxqLXutqZS9PNqzMaCM/yYtZ8cEWxpG1gV3Zfg4JIpwmq1mnfffZe+ffsiSRLTp08nICCAIUOGsGDBAsLCwpg3bx7jxo3DbDZz991307p1a0wmE3feeScFBQXIskyfPn2YNGlShe0L82wAqc5CY7GB0VW+ziAvHUeL6qbI2LUI9tEz0Kdka3qjWsWzPZrw7MXvv9p3nulZGRxNL6i0+JBlmdw8C8dSCziRZeJMvpnEIgvJDgdpokSGFkz6y6v5KrWMvwYayCItVBoi9VqiPfQIyLyx/giCWmTRY/F0DnF6x95feZSvlm2lXZQvfa7TRnkKFUMRHwoK9ZnCDLKXvIZPx1uhYQ93W1Np/j36N/598etjx5cwasszpGYeKSU+AG699VZXsOglli5d6vq6a9euHDp0qMR+o9HIli1byrUjI6PsdNoo34sl1jNTqiU+InwMYJfJKLISaKjfAjE1z4IgycRH+5XaJ8syeXlWTqQXcDLLxJn8Is4VWUm220kVJDI1kG+4LC5EUcZPA0GiQBOVlj46DQ099TT1MxIb5EmEtx5VGR6Me3/bB2YH7dsGu4QHwFM3N+fX9ftZdyxdER83OIr4UFCox9iWPY+AjHrQa+42pdo08Hf2IUnLTXCvIcWI8nUWH9uYtJGhsV2rPE/Di4XGjmYW0iOipPhYsmQJU6dORZIkZsyYwQMPPFBi//bt27nvvvuwWCzcc889vPjiiwCcOnWKsWPHkpOTw4ABA/joo48QBIGxY8dy7NgxANLT07npppv49ddfK2zr0YIivOwSB8/lsuRwKolmK8l2B6miU1zkGQRXQLMgyPiqIAiBKFFDd52GaINTXDQL8qChrxG1WPnlkbb+HqwHdu9LhXGXt285lUmeyUq3JrXb0VjB/SjiQ0GhvpKwibxdvxFwxxvXbeO44vj4NUKUZbIKUtxtiouYiyXWj+UcrN48/h4AHM8qpEfEZY9CdYqnzZgxg5dffplhw4YxatQo/vzzT4YNG8aiRYtcY++66y4GDBhQwharJLEvv4g9eYUcKTBzpshCssVGls2OySEh6QFUPGXJBg34SDJBKoEIUUNXrYZoo47GvkZiAz1o6GdAp1JV670pi3/dFM3cP48R1ayk92X+ygM0DA/k5pbBNX5OhfqFIj4UFOojJRrH3etua2oEUaXBV4acalYUrUl8DHpkhweB6ubVmqelv7PKacIVhcaKF08DXMXTxo1zPu4XL54GuIqntWrVis2bN7N48WLAKTL++OMPhg27XN/FYrGwfPly5s2b59r27PHz/C+p5PsrAkaViL9GTZynhmiths5qHTcFe9PQx4DODb2BvjrkbIh39szlIF1ZljmVmMbY/u2VYNN/AIr4UFCoh+Qvexdr2lmEtg9RuHkFap8A1F5+GPz8Ufm4vw6C9cBmrAe3o45ojDq6GarQxgia8mMdfFGRba5eUa+aRiV7kmXOrtYcIZ46UAmlqpxWpXja+vXryczMxN/f33UTvnIcwLJly4iPj8fX19e17YLFBsC/G4bQ3ttIB28jvpr69zE/f+UJAN6a0M617Y/9KRQUWWgd4eseoxTqlPr3W6mg8A8nP8vMsXXb6eBpQjw6B47OASB5qy+JCUYElYxKJ6AyiKiMWlSeekRPIyofb1Q+Pqj8/FH5B6EKCEYVFIoqOBJVcBSCh1eNPVFeePoJCk8VawAmyqgNoPbSYL5gR9CI+PeLRR3UAHVwKOrQKNThTWhgEclRuTcl9Up0gje51uqJDwC1QU1qft1V5/zhhx8YO3ZsiW2NjE4BODTIh+aehjqzpTJY7BL5uRZEUeDO5qEApOWZeeuHTXRvF0PvGk7/VaifKOJDQaGekfC/10ngZlpN/Ra5KAdrdjr23CzSN89G7ZtF6NC2OHKyceTm4cgvwFFQhC0xDcexZCSLjMMCUFpkCKKMSn9RtBg0iB46VJ4GVF4eqHy8UPn4ovL1QxUYjKpBBKrgKFQhjRB8A0uJFkGjwhChI/jpZ7GdP4M9JQl7air2rCzMF84j2yRyNx3BbjoM0uWxU4DjzW1wT+2+h5XBqPKh0JZX7Xn0RjVZ+SW7y1a1eFpAQABZWVnIsowgCKWKqhUVFbFy5Urmz59f4nwtPZyCY19+Ub0VH98dSkaQYcLQZq5tby85gIzAzDs6uNEyhbpEER8KCvWJwgxCMz7Dr+Ht6L2N4G3EEOy86ST4/4zdBP4vfHzNKWSbBSntPI60RBzpyTgyLuDITMORnekULHn5OPILcRQUYUnOwWFKRzJLOCwy1xQtRjVqHwNqX08KjuaiD9FgGDCGK29xxeuOypKElJGM/dxx7Emn2fz1ZwRcqLvW8xXBS+tDkim1/APLwdtDS26BtcS2qhZPEwSBrl27uoJMv/32W+6557JiW7p0Kb169cLLq2RzwbZezqqlRwvrvsldRXltyWEAXoxvDDhjPfafSKR/p2b4e9TvNGWFmkMRHwoK9YljyxAFCdFaemlC8PZBLCq/f4ig0aEKb4IqvEmlTi1LElJuDo4LZ3GknsORdh5HegqOrHSncMnOwZ6djzU5A5VORh9ZvntcEEWnF6VBBLpO/TDt2YjfqR2Vsqu28dP5kWCqfl+WAC8dqWklhVVVi6cBvPXWW9x555088cQT9O/fn6FDh7rm/eGHHxgzZkwpGxpfrDFy2mQpta84VU3/Xb16NdOmTUOSJIKDg1m4cCH+/v68/PLLLFiwgMBAZ+ryBx98QM+ePUudV5IkHPnOuBStyhnoejy1gLS8Ivq1DLmmzQo3For4UFCoR1gO/IFOZcdqKy0+VL6+iJaCMkbVDIIoOuNF/PyhRftaOYfazx+PIgnZZkPQaGrlHJUl0BiAnFuA3SGhVlU98yPYS88BswNJkhCLZZBUpXgaQExMDLt27SrzXMXTbYsjiiIaQSDJYi1zP1Qv/ffJJ5/kxx9/JDY2lqeffpr58+fzzDPPAPD000/z2GOPXf28Dokms1cjAL7Bl/vKzFl2EINOo9T2+IehiI8bFLPZzKZNm9i+YT12ux0BAUFwvhBwfV18uyDAYcNhUjxT0Ol0gOD6p0IEUXA65QUBEbH0HBf/Xd4HICAKYsljLv5fkJlBx7Qwgu1XNB6Tr3ZVpXdU4tCr7oiSjxPjnYWHz8WaAxftdl7Axf8F8Yptl48REBBEAVEUEAQRQXReoygWf29F5xzF5yxjPtOJLfiqHZjKEB9qX19EexGS1YaorR837sqiC3R6S8xZmRiC68eTbrCHP4JoJykvh2i/qmcSRfjqwSGTXGghwst98RaeKpF069VLvVc1/bd169YIgkB+vtNLlJeXR/PmFUtRzrPY6PD2GoRCp12bH3N6RQotdrYcOMUDw7qgU6s4l2liT2I2+WY7JqudAouDR/s0Qa+p+VojCu5FER83GHa7nV27drF+xV/YrFY6du9JQEAAsixX6LXqxGbMWoGhrW9Fkp1dOiVZQubiMchIslRqX/Hvy9rnGis5XHNtzt+Nt1aksVfjUtchyFdRD2VkawiCcHWtcbXhxQaEpvyJ0VSArmEsyLLzxVX+v2KbfOnaHM6XJMlIklTiPZUuNTu9cj5kuPheIcsIyOgi2pBRoEFdcLaU7Wp/XyTAnJ6FMfz6LMJkDHQKjpy0c/VGfIQaNfTytHEq42y1xEcjX6fgOJJpcqv4CNCqSTJf3fNR1fRfgI8++ohBgwah1Wpp0qQJH3zwgeu49957j08++YTu3bvzzjvv4Onp6do3euEubIV2OnUMZeHt7dBc9DAZtSoahQey72QKUp+mjH53KVazGREJCRGtbCXIS8fdXate+l6hfqKIjxsEs9nM7t272bJ+LYV5ebTqdBP9B9yMj0/57cyL837C+xgFI4+1v7r7tKZYe7IdwY2bcuuYp2v9XNci+bWfMTnUeI39n1vtuETWx9Mw5JeuuKkL8qcIKErJuG7Fh2egM7UyLy2RUDqXc3Tt4pDsLN33HL7ZP3G7n4w1by9Q9eWmS1VOT2YXcnND9y0hhGo1nCon5qOqvP/++6xcuZL27dszffp0Zs2axfPPP88jjzziCqSdPn06r7zyCu+88w4AC/YkcuxIJn27R/LF8DYl5hMEAZ0godJqOJySh81soklMU566uRkdo/3o8vRC3vx1tyI+bkAU8XGdk5eXx9atW9m1aSN2u42W7TvQs1dvGjSoWlMmtaDGKl/9qelGRFJpUdvrT+0JweCLjtIBkLoGARQBlrT6VaSrMvg0iMAEFKZfcKsd+8//yaGjz9JALCBZFY3WnoTNUr3fgRYBF6ucZrs30yTaoGVDDmRZbfiXsTxX1fTf9PR0jhw5Qvv2ToE2evRoXnrpJQCCgy+L4fvvv5/JkycDkFxg5tVfD6I1qpk3sGUpW2wOiaTMAtrGNuT95UcQgGeGNKdjtNMD1atTSzbuPFz1N0Oh3lL3dXUVaoTU1FR++nEx/3lrFrs2b6RDt+48MeMZ7hg1usrCA5ziwyE7atDS6wBRiyjZ3G2FC8Hoj0Yw47CUvInpg51P09ez+PC92EvFlOOeEuvZpiS++3s4qcceRyXb8Yh+ibv7riHf5k2RuXo2+ek1oBFJznOv+GjmoQdgT37ZdhRP/y0oKGDZsmUMHDjQtb94+q/D4WDhwoUMHz4cPz8/0tPTOXPmDODMfImNjQUgJeVyv57ffvuNuLg4iqx2ur22GsEi8dHdHfHUln7WTc+3kG+xs3xPAmuPpeFhNLiEB8CKA8l423J58KudFFn/YZ9LNziK5+M6QpZlzpw5w8b16zhz/Die3l70HzqcDh06oNfra+QcakGNg3/WH7ms1qHi6gF6dY3o6fzwtWRlYAyNdG03hDjFhzXz+hUf3gY/TujAmp1Zp+eVJIm/DryELX0hvsjked/CbR3eRad2Zl2YHT6obNUXRBqDirS82lnyqCitPZ3XdLigiP4B3qX2Vyf998MPP2T48OGoVCrCw8P58ssvAedSy969exEEgWbNmtHt0Rdp8eJyACQPNQMaBZZpa5ivgf9OuoWXvt+EvzWbXp3bltg/d0JHHv/KimnPQdocTOT4WyOUvi83CIr4uA5wOBwcPnyYjevWkpacTFBwMCPuHEerVq1Q1XDHSbWoxm6vPzfiukBW6RDl+nPNam+nyLBlZ0Ax8aHz0mNX6RGzKl4KvLx6DpMnT+bHH38kMjKSnTt3urZPnDiRv//+G29v583rp59+okmTJixZsoSnn36aw4cPs3//flq1alWpaxMEAbNexJpfd8tch5NXsO/wDBqIeWSqIujb7r9E+JW024YvolR9UWcwaMgqcLP4uBjseqLw6qXeq5r+O2rUKEaNGlVq+9dff13i+5avrQDgydGteLLjteM1ujcNZMnTw9h9Lpv4xiVjZQbGhXDsrZE88OUO9u09xLfbznGXEv9xQ6CIj3qMxWJhz549bF63lvzcXKKbNmXCvx6gSZMmtab+NYLmH+f5QK1DVY+uWe3jfEq05Zb0DgiCgF3niZidU6F5KlLPYfz48dx///08/PDDpcbPnTu3RBdVgNjYWH788UcmTZpUyasqZpdOjb2g+kW9yiO36AJLd04iwHoAtaxFF/kMd8c8UOaxsuiPKJ+p9jl9PLVk5NRdf5ey8FSrEIGz18h4qW1MBc5lzEfaRVToeL1GRbcmZXtHAD66qyOd9x3j72Opivi4QVDERz0kPz+fbdu2sXPjBqxWCy3atqdHr16EhobW+rnVohp7PfIC1AWCWl+vxIfWzykOHHmllyYcOk/Iq5jXoLx6DgDdu3cnISGhwrbFxMRU+Nir4dBrkEymas9zNSRJYuWh1zCnfo0PEjmefbm14xz0Gs+rjlGp/NGxt9rnDvDSkZRc+8KqPAwqkVSLe+KYNp13euYiIr3R1ZBnVqMSCQkJpCCv+j14FOoHivioR6Snp7NpwwYO7t6JSqWiXZd44rt1K9Eyu7bRCBokpPIPrAHkClfnqF1kjR6VUJ/Eh/MJUCooLT4koxeqCoqP8uo5lMe0adN47rnnGDJkCK+99lqNLfHZDRrEotpZmjh2YTW7D02ngZBDrhhKr3Yf0NC/7PTZ4ktSfW9rx+2Dcl2N3ODqJcZ79uzpKrSVlJTEhAkTmDNnDk899RQbf1tGXqGVRl/I+Pj4sHfv3lq5zvLwVavIctPy6T3ztwLQIdqvRufNSM+iXXzllvkU6i+K+KgHnD9/nvVr1nDqyCGMHp70HjiYTp06YTDUfaEitajGXkfBl1aNhE12f5aJoDagFupGcFUEtdGITdYjFZYR22H0Ri6s/YDTWbNmERISgsVi4d577+Xjjz92pU9WF0mvRVVUs0sC+eZ0lu56BD/zHlSSBlXUNO6JfeSqx1+5JBUbF8Og7jpMllw89L7A1UuMb9iwwTVPjx49GDFiBOCsgRE4+jE+/f0og+1/08CNXWUbaDWkFrjnb8thc/4tzR1Ws0LBy6AlxLt+dupVqDxKqq2bkGWZU6dO8fmCT/nffz8gMy2VYaPH8uTTzxAUFMStt96Kh4cHISEhTJ8+Hav12h/WKSkpTJ8+nXbt2uHl5UVERATjx4/n7NmSlTInTpxYrOR3ydebb77pKn1eF6gdAlJ98H5o9YiCjGR3b6BgcSyyFxSVIT68vBEr2ATtavUaKkJoaCiCIKDX67nnnnvYsaN6zeBOPTuRw5Nu5tzCWagQEGtoSUCSJFYfmsWajd3xLtpDlrEnw3pvp881hAeUXJLy9PSka88u7NpVRFqOM2W0eIlxlUrlKjFenKSkJM6cOUOvXr1c27wuppP+8fNPjB07tkausSpE6jXYZWcdjTqnlu4qVoeEVq3csm4UlJ9kHSNJEkeOHGH+f+fx7YJPMBcVccdd9/DYlKl06NCB/Px8+vXrh9Vq5eeff+aNN97gk08+YcqUKdecd9euXfz888+MGTOG3377jffee48DBw7QuXNn0tPTXce98MILbNmypcTrySefBJwxAZd0x5IlS4iNjSUmJoYFCxaUOt+lD++mTZsyc+ZM1/aJEyfSuHFj2rVrR7t27Th16hQA7777Li1atKBNmzaMHDmSvLw8BFnAKNZMinB1EDXOpylHUe01bassNtELzDmltovevojmitlZXj2Ha3GpboMkSfz+++/ExcVV2PYrsSQmYP15G9Le8xS+/BWR+1Lxs+Txxaa72XZuCQ6pakteJ9M28sPaLpC6gBwhkGbtFnFn/BcYtaXTS6/kyiWpqMiGZGTYycy7UOb+spasFi9ezB133FGiidw3289izz2Lr4eRFi1aVOm6aoLGRuff1RFT3Qe/jurdGFmAtBoOeLXaZXSK+LhhUH6SdcjBgwf5cM77/PDVl2g0Wsb/60EmPfZ/xMXFuT7APv74Y/Ly8vjll18YOHAg999/P2+//TYff/wxycnJV527R48eHD16lGeffZZ+/foxZswYli1bRnp6Ol999ZXruCZNmtC1a9cSrz179tCyZUvatm3rFB92mDJlCmvWrGHPnj288847ZGaWjD+45JI+duwYS5cu5cCBA659c+fOZe/evezdu5cmTZxt3Tt27Mju3bvZv38/LVq0YPbs2cjIzoZrFzl69Cg333xzpTw+ALm5uTz00EMEBgZiNBrp06dPmWvtBw8eZNiwYQQFBeHr60uvXr1Yu3YtgtZZFttej8SHXeWNaMkptV3l64PKUoB8td43xShez6Fdu3ZMnTrVVc/h0u/SxIkTiY+PZ//+/URERLB48WIAJkyYQJs2bWjTpg0Oh4PHH38ccKZjRkREsGXLFgYMGMDo0aPLtePCzx8ja2SifvueiD+/xf5IT7KGexNq3kzBySf4cVVLPl83lNXHPqSgAlVGTdYcftgynlMH7kUl5UHoE9zTbxNNAjuVO/ZqeOqdgiWnoOKVV3/44YcS3o1VCZmknssnNHsvd97pPq8HQIuLhcb259deYO/VaBvihSDD9gs1GxxqkxTPx42EEvNRRzgcDn79/jt8ghpw/6OTiYyMLPO4ZcuWMWDAAPz9L1f5GzNmDJMmTWLFihVMnDixzHFlBaVGREQQFBR0TdGSlJTEhg0bePXVVwFnM7iCMwVV7np5Nfr06eP6+qabbmLJkiXIDUG86GrJzs6mX79+xMTE8PPPP5OUlMSUKVMwmUzMmzfvqvMCjBs3jp07d/L2228THBzM+++/T79+/di3b5/rfc7IyKB///40btyYzz77DK1Wy9y5cxk8eDBL50wlDJCK3J+lcAm72gdVWZ1t/fwQJTuyyYTg4VHuPOXVc/jiiy/KHLdmzZoytw8ZMoTz58+Xe97iFC5fg72dJ97B7SAYWj/xCa0Bqy2fvecWkpX8OwGW45D0LmsT3yNLCME/oA+dG99HsHeTEnOtPzaPzHPz8BZtZOo7c2unD/HUVT6w8colqdysXPz8NRSY0srcf+WS1blz5zh//jzdunVzbXtl5VFEvYrMHWsY+9pflbapJml7MTbi+DVqfdQWXcN9AdidksuwhldPn60MdoeEJMmK+LiBUMRHHaFSqWgf340DO3cQGHj1P8ijR49y//33l9jm6+tLaGgoR48erdQ5jx8/Tlpa2jXdv99//z2SJLmEhYyMLcdW5a6XUH6WxJdffsm4cePYVbjD5fko7vG5JLzsdjuPPvoozz777FVjFbZu3cqyZcv4/fffGT58OAB9+/alUaNGzJ49m//85z8ArFq1irS0NLZt20bDhg0B6N27N/7+/izffoR+UeCo4HJGXSBpfNCZUktt1wT4IgOW9CwMFRAf7qbw5EGE0/l4jB9eap9W40XnJg/SucmDyLLE8QurOHT2e/QFuzFmfc/+zO9Jk73QenUkKqgvJxM+IoQLmAQ/4lq9z6DgnlW2q/iSlI+PD8v/+otnZwZTZHFWOS1eYjwuLo6FCxfy6aefusYvXryY0aNHuzJjtqXkcO50Du2D80n09aFp06ZVtq0miNLrADhTS1lF16KJvweIAkdTa07MWy/GrijLLjcOyk+yDundpw+Sw1EiWv5KsrOzy/Ri+Pn5kZVV8SwHWZZ5/PHHCQsLK1HX4Uq+++474uPjadSokXNcNQNAZ82axZEjR9i2bRunT5/m448/LrH/P//5D5IkMXbsWGRBRrwoPq7m8ZEkiRUrVlz1fHv27EEQBG6++WbXNqPRSM+ePfnjjz9c22w2Z4Bj8S6/er0erVYLorP5lmQpW3yUF/8yefJkgoOD6dSppNv/tddeIyoqqpTYHDt2rCsmJjw83JUtURxZ74PaUdptrQ10vj/m1OujxHrqTx8i6WTChl27S7IgiMSG3sLtXT9n7IB9tOq0FIv/KBCNeBasoyDhJTwc6diD7md8v+3EVkN4QNlLUmqPQN58+iuXp/BSifFmzZoxaNCgEp69H374gTFjxri+f275UQSNSGTizhLb3YUoCGgFgSRz3We8iKKA3ktDYlbNLflY7c7QdEV83Dgono86xNPTky59+rJ17Rq6du3qKl1dG7z88susXr2av/76C4+rPCEfPXqUPXv28MEHH7i2ycjofHUkHal810vAVQjtUpbEpRgCgD/++IOvvvqqhJfk0pNjVT0+ZrMZURRRq0v+Kut0OhISEigqKsJgMDBs2DCCg4OZOnUqr7/+OhqNhtmzZyMIAmOGD4Q9f+EwF5aavzpVQgcOHMi//vWvUstRixYtcn191113MWDAgNIXpvdDW1Zn2wAfLIA5K+eq70l9omjlFuROgRh8oyo1Ltg7lmHt3wLAZM3l4PkldAzpj68xpMZsu3JJ6osly5n2clfX7/LVSowDbNu2zfX1oYwCThzPpHvHMD64Y3aN2VddPFUiGbbKpc3n2+zsLyhif34RJ0xmxob408X36sXZymLJkiWcnfMopyWJBdLLpUr6V7Z+SkJCAhPuvocTR87w4o4Y4v/4pcRDhML1iSIj65ju3buj1mhYv3Ztmfv9/PzIzS291p+dnV3CK3AtPv30U2bOnMn8+fPp37//VY/79ttvUavVJYLmZFnGq6FXlbpewtWzJHbt2sW0adP47bff8PR0fpjJ4PJ8VNXjExMTg8PhYPfu3a5tkiSxY8cOZFkmJyfHNc+GDRvYtGkTYWFhBAUFsWDBApYtW0bjiy5yyVL6Se3KlMxL8S/F6d69ewkxcombbrrpmlVpLRYLy5cvL9PzgdEPHQXIV2SC6AKcH7rWzPpf6TH34EbE82a8hwyp9NjiwceNo2L58eN9GNXl//7fddddxMTE4OHhgZ+fH7169bqm56wEoj8qKt435xJPLz+CIAq8dXPzSo+tTfy1avLsJX9/sm021mbm8Z+EC0w+nMDwXce5acshmv69n7C1e4nZeJA79p7ilVPJfJeSxYT9p5GkiqfrXhLrN8/8hJAHPqhUsPqGDRtcgeqxsbGuv4upU6cyfuIDNLv3LQbdNoa33nqrem+MQr1AER91jF6vp3v/AezdvrXUHyVA8+bNSz3p5+bmkpKSQvPm5X+4/fLLLzzyyCPMnDmzlCfhSr7//nsGDBhAUFCQa5uMjKgSy82SuJpL+mpZEjNmzCAvL49hw4bRrl07Z8EqgWr3qLnlllto0qQJkyZN4uDBg6SlpTFt2jROnz4NXJ4/LS2NkSNH0qRJE5YuXcry5cvp06cPt956KycSnanIchnLLtWtEnotli1bRnx8fJmiS/T0RxBkbHklb4aGIGdwpTW7/ouP1J/mIxkgdMijlRp3Kfi4sunmAFarlSlTpvDbb7/x9ddfu35vr7XUeQmV2h+tWLmGdwm5Jg4cTqddqwZE1rMCWAEaNWZJpsOmQzRev5/QtXtpsfEQ4/afZtaZC/yUmsOuPBPZNgd+GhUdfYyMCvbj2cYhfN+mMfeEBVDgkNicU9ojeDUuifW2sY0RZS09+t9cQvxVpX7KkSNH6BTvXGaL79mLn3/+uQbeHQV3oyy7uIEuXbqw7e/1rF65kjF33lli3+DBg3njjTfIyclx3ZQWL16MKIrccsst15x33bp1jBs3jgcffJAXXnjhmsdu27aNU6dO8dJLL5XYLiMjIFS56+XVsiRWrVpV8jyyTJuv2iBc1L9V9fhotVoWLVrEuHHjXAKodevWPPnkk8ydO9flkXj77bfJzs5m165d6HTOYLz+/fsTFxfH7I8/54d2INmKrnqe2uDKVM3iqDyd12zNzkDrezlmROdnRBJU2HPqritsVZAkCeuaPYhdw9EYfCs1tqrBx+B8T4szePBgGjVqxNdff03PnteOE9Fpg/CwlSyxXh7TVxwFGd66pX55PQC8LsZH5DscBOvUhOo0NNTraO6hp7WXgdZeRjzVVy+Zv+dimu5xk4Ue/l4VOuclsd4+1IevAYunf6WC1aF0/ZQ2bdqw7I/fgAb8veLPGhP/Cu5F8Xy4AY1GQ5+Bgzi6b49rmeISkyZNwsvLixEjRrBixQo+//xz/v3vfzNp0qQSH7r9+/cvEVF/5MgRRowYQUxMDHfffTdbt251vS4V+irOd999h8FgYOTIkSW2XxIftY180ZW7tWgfUD2PT8eOHTl27BjHjx/n2LFj7Nu3j6KiIjp27IhG4wwmPXz4MM2bN3cJD3BmILVp04Yzic4PM9lSWnxUp0rotSgqKmLlypWlBN4lXJ1tszNKblercKj12PPqT2ZOWWTu/B1VqgPf4aXbr5dHVYOPy0KlUuHr61uhejEehiC0Khv5pooJu9RCC9v3pxLbPIBY/8rFRdQFffycMWUftoxmS9eW/Nw+hvdaRPFQVAPi/byuKTwABvg7x3+cmFbpc8eHO5cHk/Mqn+p7pSh/9913Wbd6Bce/eY6czPSrxrApXF8o4sNNtGvXDh//AFYtL1kPwM/Pj9WrV6NWqxkxYgRPP/00DzzwAO+9916J4xwOB/ZijaO2bdtGbm4uBw8epHv37sTHx7tel2p4FB/7ww8/MHz4cFf8xSUkWaoT8XGpqmWEJhhwPqGuWrXKFaMBFff4gHN5JSYmhmbNmpGRkcGiRYt48MEHXfujo6M5cuQIZvPlD0OHw8G+fftoGO1s0S05St+gqlMl9FosXbqUXr164eVV9hOlxsfpsbGX1dlWa8SeX39qkpRFxq9fInkKhAyYWOmxR48eLSU4K5NuLssydrudzMxMZs+ezYkTJ0oFA5c1JiXHucSVmptyzWMv8eWBJLBJpGSa+Opg/Xsab+3lXAY6VFA1j14bbyPNjDrOma2syKiYILsk1kM99QhakcTE8yXEelXqp4SHh/PWx1/T7K7XmTDxX1etkaRwfaGIDzehUqnoP3gIZ44dLdV/pUWLFqxatQqTyURqairvvPOOMyW0GOvWrSvRCn3ixInIslzm68pCUiqVipSUlBJZF5eoq06zkuz0fDTWO7MgqurxAXj99ddZtGgR69atY/78+XTq1ImOHTuWKMj2wAMPkJ6ezm233cYff/zB0qVLueOOOzhx4gSPPPQv4LI3pjjVqRL6wgsvEBERQXZ2NhERESUE5JWpmlei9Xd6Phz5pQMgZa0BqaB+ez7sO06CIJP8xwflH3wF1U03/+yzz9BoNAQGBvLKK6+waNEi4uPjrznm21Uv00jzAZIsYNRVzIvx5E0Nua1fI0yFNl78Zi8t3lnNa5tOuWpSuJtWntUvNDYw0OnB+DgxvZwjnRQX63qdxPmdGyscrA6l66eAs0Cg5WLg7Pz/zOahhx6q8vUo1CPkekZubq4MyLm5ue42pdZxOBzyh/+ZI3/y4X9lSZLcbY4sy7L8yI+PyD0X9Kz185iKCuVWX7SSP1wyy7Xt8OHDcv/+/WWDwSA3aNBAnjZtmmyxWEqM6927txwdHV1i29SpU+WIiAhZq9XK0dHR8nPPPScXFRWVOufq1avl3r17y/7+/rKvr6/cvXt3edmyZbK9MFuWX/KWExe9WBuXWiUcdrvseNFXTvr+vVL7dvYZKe8Y8aAbrKo4aX8vlA/0aikfuqN7pceq1Wp51qxZpbbHxcXJDz5Y/nWnp6fLO3bskJctWybff//9sk6nk5cuXVrquHxTgfzNX/8n/7ysvbxqdWN52crmcr6p8p87NrtDfmfraTlu9mo5esYSufHMv+RJSw/I6SZL+YNrmbA1e+ShO49VebzD4ZB7bzsiB6/ZI087eq7c47dm58s+r74v6yKjZVVgmBxw6xOyw+GQBw8eLCclJcmyLMtbtmyRW7ZsKTdu3Fh+6aWXSozv3LmzvHXr1hLbFi5cKIdHN5a1viHypMcelx0OR5WvR6F2qcz9Wwk4dSOiKHLzkKF899mnHD9+nNjYWHebhFbQ4hCq1uirMjhk55KRWKy3yyWPz7VYt25dqW2zZ89m9uzy6yv069ePfv36ldouWZxLGGV5PtyFqFJhxohsyim902BENtVvz0dQz7Gk+L2O2r/ytWyqm24eGBjoKuw2aNAgsrKy+Pe//+1snFiMY0lHCNH8CUCyJZ5R/eZj0FU+nkCtEpnWpRHTujRi0dELvL/2BEvXn2XZ5kSaNvPnobgwRrULL9GArq4wqkRSrVUvNCaKIovaNqbt5sMsTMnkndiylzwkSeLuA2dYm5WPvnsf9N37oDpbgOZoLnvS8isUrA4l66dcYuzYsXi17MUzX6zlzVdHu+V9VKh5lJ+im2nSpAnhDRuzatnSSuXT1xYaQYNNqP2qiNJF13Rx8eEuBMEZeCfL7n//i2PDE7mojLV2oydCGTVJagLJYqlQ07qKIGTbUAWVrn9SHtVNN7+Sjh07cvLkyVLbTyauxCapuanLVu4e/E2VhMeVjG0ewtZHerLo8e40j/HnxNFMpq49Tr9l+/h2yxmsltoX9sXx06jItlXvnME6LbFGfZmxYKkWK08eOUf4+v2szspHArxUInu6taShGdReGto1qFimzLWw2p1/m0pvlxsH5SfpZgRB4JYhQ8hMvVBmJ9a6Ri/qsVO5qohVQZKdH4hiNet81ASuzrr1QPwVxy56grm0+BAMRkRzzYuP5LOJ7OzWg99vH0NWNVN57fZCxFwZTVCDSo+tbvDxlWzcuJHGjRuX2l6Uv4EMSzO8PYLKGFU9uob58tc9XRgX6kdwto1AQWSqOZdOa/cze9UxMrPrJq07WKvBVM0YlEK7gxMmM/6aktkx048lOj0iF7JKRIpt6dqCUJ2W9EwT9nwbhdUUPwBmmwNQGsvdSCg/yXpAZGQkcR06smTxojKf0OoSg8aAXagD8XHxRi+K1073qxMuCqD65vmQ1B5gLb28Inh4ostNYU+X/uzt2JP9bTtzoFUHDrZsw6Hmcezqe3uVzpe0fSdehQU0O3KQbePvJunY8SrbbslLQjQLaIIrn5Zc1eDjP//8k7Fjx/L111+zbt06fv75Z0aNGsXy5ctdJbwv4ZBkIj2PEWo4zOJ1b2Ky1E731zAfPWa7g8WD2rCqaTQ9BQ3/EUx02nmUaUsPciKh8hVVr8WyfUm8v+44z6w4zIPLDnAmy4QEFJTR46V4FdmQkBCmT59eZkrymSILEpBhszN27ylG3DmOmJgYZreLJe3WXgQ9+yjPZJ/FIAp09/UkUOtMb+/Zzlnd97vDFcseuhYmqwOtSkQluv9hRaFmUGI+6gkj7xjFycOHSUhIcGtHTL1GjyzIOCQHqloUBpc8H9WtcFojXFp2kerWJV4eksYL0Vw6pTZs/EjO5+WCRoeg1yPr9Qg6HSqthrwt2/BMLblk4bDZKDIVUVRYiLnQhLmoCO+gQAICA/j1Px8SsHolkbPfwXqxP86xBx/F95cfyRp5O3tat0U3YgTxI4bjadBX2HZzslNE60Iq19MFLqeb/9///R8jRozAy8uLBx54gNdff73kdV2Rbt6kSRMsFgtPP/00GRkZBAYG0qZNG9atW0fv3r1LjFWJAmeLbibasBJ/6VMWr9hAs5gZmK0FtG7YiUDvyntsyiLIz0gBUJRdRKtIP/4b6ceLuUV8svsc38omvjudQP/9Z3k4qgHd24Rid0hk5FlILzCTVmAhvdBKhtlKptVOht1BpuQgU5bJFmX+z9eXe+Ibuc5lsdq5LysdvUOmgQMCJIEIK2SoISvDhGfE5X4ol6rIxsTE8PPPP5OUlMSUKVMwmUzMmzevxDU0NujRCAI2WWZ9dj55FzK58/6HyNB40UgF0X8vZ+qYO1i7di0927d1jUvMNIEAg5tW37NUZHOg19SDBxWFGkMRH/UEURTx8fGmsLDipYxrA0EUQHbGZNSm+LhU50OkHnygXLrOeiY+ZI0XKtP5UtuDe7UnuFf7MsesfmUeft/vZlPX7mitFnQWMxpH6evKAg6ER2L08CTkzCmOvPgKbd99m0JAzsyg5dIlbPzkc4wrlxPxyoucen0mF559gV53jMCg05aa70osF84BoAsrvdxREaoSfNy8eXN+/fXXCp/j/qEfY7IUsu3I73hZ38eUfB8Af53vxF1DSqehV4XgQCMAF1IKaBzg/DrYx8ALfWOZanXw3d5EFlhzGJWdhueqVAo0pcW4ziHjbwd/BwQg0kRUsUWwsyInn+75ReTmW8gptJJXaEUlyTyl8eSJATEAHEjK4ebjCSRmFxFVTHxUpoqsUS1yvEcrsu0OzhZZuf2lt1kKeAjww03NafT4g2VWkW0c7MnxwxlsT84lOrZ6peeLrA4M2nrwWaFQYyjiox5hdzhw2Gt/yeNayKKMKIvY7XZXddDa4NKyS/3wfAhIsoAgufe9vxJZ64VaqpwYPdmpK2uyk+jg64XG0wPRoEelN6A26FEbjWiMRjRaDZlnE/H771zCkxIBaHpoHzvefJuWQPOff8Dn5ee4fdrjSFMeY+vPv1M07wOiZr5EwsyXCN2yBV8/32vaYb3gFE36kKqJj7rCqPOgb7txWG2388PaZwhV/0ZA0LAamz84xBnEmpZWyJXvhFGr4oHODblflvnraCqH0/IJ0KoJ1GsIMmpp4KWjgY8BD6Om1N/JpKUH+NXgYNXOYyUnFQV8i92kowM94DicyzPRvdhhV6siO2nSJFasWFGiRg6AQa3CoFYRptfyf1EN+CgxnTnNI2nq4fSGlVVFdk6/5rTYmsj/tiQwOrZ63YhNVsXzcaOhiI96hIe3L1nplS9lXJM4cCAi1nrmjSvgtD7EfAASAnJ9Ex86bzRy5cSHPjiYz24by8OtY4gKvHb2xqoiE75z3uV0dCNs7TvS8tcfATg69DZaXCxqJ4oi3UaNwDxyOGfiWgGQEh/P+mEjGPLGTDTasgWqLS0VWQtqb99K2e8urDYzRulvzpvacnefCTU2b9BFb0daxuUA4SVLljB16lQkSWLGjBk88MADDGkRwpAWzhv09u3bue/20i3nzWYzkyZNYsuWLUgSPP74TLreFE/KuWO8/cbTWC1FtG3TlvHffOU6l7dOg7dd5ry9pDA4evRoqcaTFa0i+2yTMJ5pHIrD4SAzM5PPP/+cEydOMH/+/BLH6TUq1GoRSyXfs7IostkV8XGDoYiPeoR/gwacP3XCrTaYbWZEWSxVUbWmcTjqT7YLgCwLCPVs2UXQeaERTMiS7FwOqwA+OjXkQ7bJShTXFh997rub1ZJEyyGDaNQwinP334uo0zEyunQtB71Kxbm3ZlOweQsxS3+n2ZJf2bFhHXlBYQT4heLX/xai7xyESiUiatTYMzKQfNX1w7NVAX7f9DK+qkJat3mzRutIBHjoEIH0HGd2y6WW82vXrsXHx4eOHTsycuRIVwNEuNxyPi4uju7duzNy5Ehat27Na6+9RrNmzfjiiy+w2WwUFhbi6+tLx8kj+WT+h3Tp0oXXX3+dzz//vEQV0DC7QKJUMuC0JqrIXmpf4OnpedUqsjazA6Ou+qLBZLai0yi3qxsJJdulHpGfnYWnj69bbbDarYiyiFpdu3/ol8qr14c6H+D0fFDPPB+CwQetYMJmqbhdfganJyK7qPwxap2OgY8+RKOGzqDQqGZNiShDeFxi4G1DueOt12hzYD+WTxfgl5tD9MnDeO5Yje3NGZxs15a9vYcjSRJSRi74lwxQtdvtrPtzKmuXPoW5KIeCvPQaqylSHQ6c2UyQ+AdZwr3EhDWr0bnXrTuDBOzPdno+LrWcDw8Px9PTk8GDB1e45fw333zDlClTAGdzykvi4dy5c3Tp0gVwFtK7suV8OCJJNSysR4wYwY4dO1i2bBljxoxhzJgxLFu2rMQx53OLwC4h2Kv/M7YUFaGrRMCzQv1HkZL1iAZh4ezZshlJktxWxc9it6BCVetPrJfSWoV6Iz7EehdwKui9UQkOzIUFaA1+FRoTcFF8ZJWRWlmTtOvZnezNm3l83RF2iRbu/3MdA/ZvwJCVwN6uN2PIS8HSwciGv16j0LoGxCI0xjS4GHe4acvvrrl6dj+IVle9gMSqYrdbOXz0WWyOKEYNfKrG539upTMm464+zoiPslrKV6TlfE5ODmq1mmnTprF582batm3L3Llz8fLyokmTJixfvpyBAwfyyy+/lGo5H65Rs0EuufhRF1Vk82x2ZECqgY8Sq9mMwbP+dQ5WqDr145NfAYBmzZphtphJS3Nf3IfVYUWsg18LV52PeiI+ZFkAuX55PkSjMzvBXlDxgl/fH0sFKl7DoSySkpK49957CQoKwmAw0KJFC7799lvXfqvVyvTp07ltxG38fPfNHBnZj6/je9J680qER19EatKaojBPTHF5WLWfI0ghGNS90MrdCPJ+giZhC7Fm9nXNt359BxwO97z3S7e+j58miYZNXkWv0dXo3B9/tYcLsoQauKlLRLXmstvtnDp1isGDB7N7925CQ0N58803Afjf//7HO++8Q6dOndDpdKhUJZc5IvVakrUg2S/HcdVFFdkoLwOiWsRkr76ot5gt6PWK5+NGQvF81BMkSWLP7t0IsoxOV7MfgpWhyFJEvjqfBxc+iM1hwyE7kGQJh+xARkZCQpIlXP8uejBkZC6VOSwyO9e3L13HpU65siwjI+PAgRULqGHzjws5u2ApIIMsgyDS91+P0qF331K21SYSYr2L+RA9nOLDkZ9T4TGH8ouQhDz+88QEmsc2K7eGw5WkpKQQHx9PbGwsn3zyCd7e3hw6dAiL5fKTs8lk4tNPP+Wmm26ie/cerFy5ghR/FeF/H+DC4+Pg8XFIksSuDZ/grY+mVb/Bpc7TsPlNyLLEmrUxiGorK/7szc2DV6PW1N0NJjnjONqiz0mwDOOhFt3LH1BJ/nuxuNa3d7RzbSurpXznzp2vuT8sLIyAgAC8vb0ZOnQoACNHjuTll18GoGXLlq605I0bN5YSFZGeeiy2AtKyTIQ0cHoPBg8ezBtvvEFOTo5r+aamq8guOpqCbJMY1+HqS3kVpcgmYdTVXvadQt2jiI96wvr16zmwbQsDbx+Fn1/FXOy1gUe+Bx6CByelk6gEFaIgIiIiCiICgnNJRnT+rxE0CAgIguDq+yAIAvYiO4Ig4GPwcW6/6HYVcc6hFtTOsRl5tAxpjC5UhyAI5F5IwZySiOCGJScZAeT6JT5Ul8RHQU6Fx9wZHsDq+Qtw5FeshsOVTJ8+ncjISP766y/XE3T//v1LHOPr60tWVhaCIPDFF1+wcuWKUvOIoshNvSdd01ZBEOnQeiu7D3RF63mBtWtuolXz7wiNbl3h660qkiSxbsd01LI3t/V8sfwBleCn34/y/OZTFAFN1Wq63HR5GaV4y3kfHx+WLVvGCy+84NpfvOV8XFwcCxcu5NNPP3W2YbjlFrZs2UJ8fDzr1q2jRYsWAKSnpxMUFITdbuett97i8ccfL2FPtJ8RsuFc5mXxMWnSJD744ANGjBjBs88+S1JS0lWryJ49e9bl1fjzzz/56quvGDZsGJGRkWRlZfHdd9+xfPlyvv/++xLn7dswgFdFgQUbTvNQm+p5fsw2O0alzscNhSI+6gmhoaEgihVab60KNpOJpIQEGrZs6dqWc/482SdPEtWjB2mHDhEcF0esFItfml+JD8S6Yv6MJ0FvpG33nuUeW9PI9STgtHga5pMPTGQyIJkuL7ts376d++4rnYa5cuVKHntyCucKTFhN+QS074q/vz/79u3joYcewmQy4XA4+Oabb5g+fXqZ587Ly+OHH37gf//7XynX/ZWUFRP0kLXycRt+QUF0br+dNUvvwzP0EIdPjcA/eD86ffWbvF2Lpdv/R7DuAFaf2QT71KzYf2vLGYqAOxv48uT4NiX2qdVq3n33Xfr27YskSUyfPp2AgACGDBnCggULCAsLY968eYwbNw6z2czdd99N69ZOMfbWW29x9913k5+fT3R0NF9++SUAX331FZ988gmyLPPAAw9w8803lzhnVKAHnIazeSYu+VjqoopsYx8jHdoFs3vPBRwOCZWq6g8VFrukFBm7wVDERz0hP99ZRjsoqPqliAvS0zm+bh3ZWdlYcnIIbdSI3w47W1h7OBw8+sQTfPzee+RfSqe9VCnyl18ASvSuLKsmQXGudjPs2bOn65qSkpKYMGECc+bM4YsvvmD69Omup6tnnnmGsWPHUpiXR/6504R3jHdLsK2EiODmmI8r0zA7tGvHnbdLJcRHWWmYcXFxPPDAAwyf+Rk/RgWSOqgLTX2cnUSff/55Zs6cycCBA/H19eW77767qvjYvXs3VqsVjUZD79692bx5MwEBAdx777289tpr5Rad+0RbxGer9vCGzpd7eza65rHF8fIL4LYJv7Nj3QLypFkc2f0j7brdW+HxlSUpMxGDaRbni27i3n4ja3x+CRjk68mbU8peyrn11lu59dZbS2yrSMv5xo0bs2nTplLbp06dytSpU69qj79Ri9Euc76wZNBpXVSR9dGqkVUCFknGWA3tUGRzKJ6PGwxFfNQT/l65HEmjJSEhgQaBgYj5+Wi0WrTePuh8vCtUd6MoK4vzBw6wZPkKcosXfzp8+YOsUKXinXnz4BrzXUqMq05Ngg0bNriO6dGjByNGjHB9f8899zB79uwS59z4xy8IkkS3W6vWFK26yPUg26V4GibAwJv7s+LU1/Tp4mwuVzwNE3ClYQYHB+Pp6Ul4aCRQhMrhwJHhjDcQBMElAvV6/TU9GhcuXADggQce4MEHH+Tll19m+/btvPjii4iiyKxZs8q9BodK4KPMbO6l4uLjEnGdxrJl+ywyzTPJujAM/5CA8gdVEkmyc3RfHwA8DME1Pn9evoUMWcKjHt0oBUEgzA6J9trNgCqL5NwiRJWIsZoFwixWBwalyNgNhSI+6gmduvfkwM4d/PbDQucGQUTWXBYIQYWFdO10E+1G3VHqBrJmzn/YlZxEkU6HpFKhEgV6+fjQsEMHrA4JqagIh7mI0ObNmbd4sWvcqJZxxI0ehSRJ2Ewmtn77Lcf27SPL2xsofTO8VJNg3LhxwNVvhpfcxOD0epw5c4ZevXpd8/qPbf4btV8g0c1iq/gOVg8ZEdzc1fbKNEvflH2cKxQwtO5f5v5LaZhBQUEUFhaSd/4UclQwkiSRl5cHwNtvv80tt9zCk08+SXZ2NoMGDbrq+S9lIA0YMIB3330XgL59+5Kfn8/s2bN58cUXMRjKXlo52C2OBg0aMP6P/ZxUV62ug8HDE9nmh6DJZueevjSKeIuY1qWDVavDLxvewvfi1/Ht/l2jcwMYDRp0gLWabexrmjBZJMkNy4op2UV4+VYvgN5id2CXwUOn3K5uJJSfZj2hd+/e9O7dG5PJROqePRx/7jn0gQF4jhiJqaCAg2fO8MeRw2x7dhf3PPUUniEh5Kens2j2bM4bDGg1GnpHRhHeuBF+EREEXCVd7uW4ONIOHcIvMhLNRZGhUqlQeXnRZ9IkzLNnuyocVrUmQXEWL17MHXfcUWIp5fvvv2fFihW0atWK999/H8lchDUjleaDSrqi6xJZEBHqWcCpznSaPF0cvuUIMkEQ+Oabb7jv0SfJslnQ6fWuCrIffvghH3/8MUOGDMHX15c9e/ZcdZ5Lgc79+vUrsb1///68/vrrnDx5soSwLM6ln6+HKFJE1W5ygiAwYOBOdq77nlzd8yQkP1+j4iM9JwEv2xck2kcycdDs8gdUAbVapKWnHmOmGdPRTIzNa957UxUiVGq2U7FU65pCkiTMRXY01fRYFFqcv8tGrXK7upGo1k/zzTff5JlnnuGJJ55gzpw5ADz88MOsWrWK5ORkPD096datG2+99VaVcsf/iRiNRsIjIsjIyiL26WfwHjQQgB6yzNElS/h561ZWfvIJI198kYILFzhvMOBbVMSE++8nKLZiXoMGcXFX3ScKAlINFhj74YcfeOedd1zfDx8+nHHjxqHT6ZgzZw6PPfYYd93SDwE4tnYlJ7duJH7UeLrcfPUn9KpSaDLz/Pwl5FvtpCecwejjg4evM8B3ukOL0ZTItv/+yxl8ehEZaJSxHkGyIQigwYEfea79h3VtaPH03zVSlO3KNMuUXCst4gKvuv9SGiY4l7be/uI3HslNx/DgGFfl0IULFzJ37lxyc3PJy8sjPT39qudvWSwYuSzMZnO51+ClUlH+UdemTfwINmx6HlGTw4rfRyAj0a7DOwRHVM8rZrPlohIlQhoMrKaF12Za+yiiN1wg64vDZGtFdLH++NwcjaaBsVbPey0i9Fp+s1rrtIDh2nPZWHOt3Dm0elVjCy9W+PVUPB83FFX+ae7YsYP58+e7XO6X6NixIxMmTCAqKoqsrCxefvllbrnlFs6cOVNuBL2Ck6wffkDQqPHo0cO1TRAEWgwfTvSuXeyTJHL/7//QZWdD06Z0admywsKjXIrdRKtak+AS586d4/z583Tr1s21rXi8yIMPPsicOXOYO/sdivLzMBfkk3F4H6f27a4V8bHjwEl+STUgyBJ+hgiwgjrbuW+9ugP95P00yNx5ecDFt8IqGrBpA8gM7Y1DUGGwZNIocz3JqnCaFu4h32zD23BFDI0kgc3kfFkLkE15OHLSsJ9PwHbyCJoGvsiFOcimPLDkIZsLaWUp5MDf2zn5VGt8dQ5Wncpncpcc15RXS8MESEtLw9ugQbpgorCwkMK8HHJycvD392fr1q0cPHgQQRBK/b0WJzo6mtatW7Nq1Soee+wx1/aVK1diMBjKFScAXmoVZrl6QkyrMxDs+V+SLsxDkLxQe28m+ey2aosPHw9nMHeRJada85RH/OAm/HU4kzaZNmS1iPlABuYDGYieGoztG+DVNxKVsW5rVkR56ijMKyQrz0Kgb91Uk92SnAPAPXFXT+2uCIVWp/jwqIEeMQr1hyqJj4KCAiZMmMCnn37Ka6+9VmJf8YZGDRs25LXXXqNt27YkJCTQpEmT6ln7D0C220n87lui7roHlWfpdMMOvXqRtmoVuQgUREURbTLRefz4mjNAEBAuPjVXtSbBJRYvXszo0aNLeAUuXLhASIize+evv/5KXFwcodENuXPKDCRJ4r1xt2KspU6o2ovu389GRNMvfvgVe+8sd/yVv72pq/6HduNOHG+GY7qoVGQERCQMQkkXt4Dzj00N6AEynCEmkl1AllRIkgq1rGZWT08Gf3EUSRZ4vKUHukRHhdIwZ82axa9//EmWzc5Nt/+Loz/MZ8SIETzwwAOMGTOG5ORkgoODSxQZu7KGA8Drr7/ObbfdxpNPPsnQoUPZsWMHs2fPZvr06Xh4XP59XLZsGYWFhezc6RRrf/zxB15eXpgKPbFFhWBzSCxftrRKmVKrV69m2rRnkCSJBg0sTJok4OmXzdixYzl2zFmuPD09nZtuuqlSmRcGvVP4WqyZFR5TFURRxK9zCCxLRD2hOQGeOvJWnaXoaBYFG5Io2JCEuoEBz/gwPDqHIFQjBbWiRPkYIA/OZRTWmfjYkZAFokC0d/UKx13yfCgxHzcWVfppTp48maFDhzJgwIBS4qM4hYWFfP755zRq1IjIyLKr3FkslhLVEy8Fyv1TEdRqVDo9Ki+vMve37NePlv36ITscSIWFqC7GbdScAZeFQnVqEoBzyWXu3Lklpp8zZw5LlixBpVIRHBxcog13XlYmAqCz28k+esTZ/0WSkCXZWR1VkkCWkSUHag8P/JuX/yReHO3FrpgWa80E3jXsNootmRlINguCIDsLruGs0mpTGZHUBuxqI+pP5uOhN9Bi4j3IKj2yQ0TfthOiXxCip0cJcTb64suSnsXpnt2xDLmfpc8/7Np/tTTM999/n2dnvslNmw9zRiugb98W9cKPePHFF/Hy8uKpp57i9ddfL5E1dWUNB3Aui33//fe8+uqrfPTRR4SGhvLKK6/w9NNPlzjukUce4ezZs67vL7VnH3b/E3DXRNLzi6qcKfXkk0/y448/Ehsby9NPP82SPw5z/505LFq0yDX2rrvuYsCAAZX6eYmiDrNdj82RXalxVUFvdQacmsw2Qpr4ETChBbIs8/PH3zPj5Wdx2Ow82mUC49oPQxvtjXffSHQxfuzYsaNMQWY2m5k0aRJbtmxBFEU+/fRTehTzjE6bNo0vvviCjIyMMu2JDvCARDiXW0SHWr96OJJVwN4DabRrH4JGXTMxH4r4uLGo9E9z4cKF7N69mx07dlz1mA8//JDp06dTWFhIbGwsK1euvGqq6KxZs3jllVcqa8YNjW+79iR+8zW6xo3w7N+/zHgCQaWqeeGBs8J5capakwBg27Ztpba9+eabrp4UV5KTnAzA4fUrOLy+dNXMK2nfsi39Xnq93OMuob/4O2ix1Yz40Bq9iR9bds2M4iyb8yN5XmF0Gl7x2hVrZnxFQyCgT9cKjwny0nH65ra8+fdpPmjUmBUrVlxzff/KGg6XGDt2LGPHjr3muRISEsrcvmjXeXbkZbB+45YqZ0oVTw/Oy8vD39cLuz3HdQ6LxcLy5cvLLRVfFkUOLxxC7YsP+8U+KhqPkmLvmfdfZv3uTXgZPenYtgNDOg/A5wxknHH+DT345UPMGTKDZoENGTl/MvFno2jRoAlv//0pwRo9q+/4DJtkx/RLPueXbQQBjqcncHrzASSTnaQXNl0u1FPsc0MWQNfDwJm0/Fq/doDPdp9HkOHfXSufcn0lrpgPJeD0hqJS/r7ExESeeOIJvv3222s2+ZkwYQJ79uxh/fr1NGvWjDFjxlw1WO2ZZ54hNzfX9UpMTKzcFdyARL06E+/YZhydOoXTDz2Io6CwDs/uvhbnKquNrieTiI9pw+Bhoxg8fDRDho9h6Ig7GTZyHMNuH8/wOyYwcKizFsiew/uQKlGbQ3fxyammxEdFkQFBqNz76n1sA0X6AILiK1dqXBRFfLQqZFEgr6juUyv99M5YhjNnz1c6U+rS/o8++ohBgwYRFhbGgQMH6N+rKXbpcqG1ZcuWER8f7+pJUhmskjdItS8+CnMs2JAJDr/swSyeuu7t58PQ24ezv3kGIc92RhPpRZqUg6SBdl07YGziz8juQ1iXuRttlDe/Hl3F5JH/QhPugTHCh8DoEDQNjGiCjMzaOJ9nRz4OApy2pjBu4RRi3hxA+/eG8/r6j7DrQGVQE2qWSXKU/ntJSUlh+vTptGvXDi8vLyIiIhg/fnwJz1ZleH3TSX5cdQq/CE/iw32r+ha6KLA4u+MalZiPG4pKScldu3aRlpZGhw6XHXcOh4O///6befPmYbFYUKlU+Pj44OPjQ0xMDF27dsXPz49ffvnF9dRTHJ1O59ZGavURdVAQjT/5lAZr13Li2WfZP3wo+rBwwu+5F++BlW/6VFkqeZ+sMex5efgXmvEdNITQYi7lK8k9mwB//oynQ0YUK/6BpL9YeK3uxYdQqfdUckj4Zh3FMWwiYhXiAXx1GrBAZqEFX4/yi9PVJL4XAykLq7G09f7777Ny5Urat2/P9OnT+eHn5Ywffflh54cffijXM3M17PggFBMytYU9q4gMERoVSzO9muBSe+sIntyOxJ12og83IehBpzeouV9n1q9fj3ZcNNp3jbx94ms2b95M27ZtmTt3Ll5eXixatIjuo/vT6eWhME9k7NePExMTwy8f/OpqKCi28mHevHl4fLmV3zJzOLRoB+3DfJh+UyN89Rp27drFzz//zP3330/Xrl3JyMjg1VdfdcV8Vbbq8s6kXBBh26Se1yypbrdLvPHJDn5LzMJDEGjnqWfquLZENypZ7t5kdaBVCWjqIDZGoe6olPjo378/Bw4cKLHtvvvuo3nz5syYMaPMbBZZdq7XF4/rUCgfQRDw6tePFp99Rt4fSyg4eoRjM/5NXFAQxg7ta+28zhTNulcfe958C/0XXwCguUZjvTXPTWfPycMAFKgEfn/oXpCdnuas3GwsDjuCSsUhrxh+DeqPl2xGjx0RmfSTe0la9w3//hKsaS+XCoCcPHkyP/74I5GRka5AyuKMGjWKhIQE176XX36ZBQsWEBjoTIf94IMP6NmzdF8aGRArmQAiixokR9V+Dn56DeRBZqGtVJBsbeN/UXxofQKrlCmVnp7OkSNHaN/e+Ts+evRoHntkIbLgjAUrKipi5cqVfPjf/2CzFaHRVC54Uha8EOULVb6+iiLmWcnV1kzKut1u59SpUwwePJh58+bx7LPP8uabb/Lss88yd+5cV4l0i8WC1Wq9akPB4Ewrp9OLOJZj4cieNBauPcPgzhE806UrR48eRa2+fDvo1q0bUVFRfPXVV9cs3X4lu09ncjolDyT415vrccgSmwudXu84jQabJGOVZWyyTNLFon79PA0cL7Twe14hv8/fTJgooi22ZJQrSXi6zyGrUEtUSnx4eXnRqlWrEts8PDwICAigVatWnD59mkWLFnHLLbcQFBTE+fPnefPNNzEYDAwZMqRGDf+nYIiLwxAXRwOHA+vo0SR/8B+afv6Fu82qcSwnT6AHbA8/hP/Fbp1lYfDxdX3tLQuk5Tpd6DKAAEa9ER8vbzKsTvdyvqCni28edruDo+u/ZNTDL/DCxEGMHD64VADk+PHjuf/++3n44Ye5kpUrV5Yprp9++ukSaallIbt6/lYMUSXiUOvAXrWiUH5G5591dlHdl9P293R6WgKbtqpSppSfnx/p6emcOXOGRo0asXr1aqIiQlB7nmLVqmZs2JBH8xYmdu3piiwLNDDOpk38iArbp1Z7IlhNNX3ZpTCaHGR6l0ynrWrqekBAAN7e3gwdOhSAkSNH8vLLL3P69GlOnjzp6m5rMpnw9vYu0ZxyzJgxTJo0iRUrVtAmJ5oDMux7eSCrErJ4afkRlqxLYOWeZL64pxPdwi+L/oiICIKCgki+GIdVETYdvsC45BT0RjVeGhFkGV2xmKNmfkY0ahGtSkSrEdGqVUQHejDuVmcNqJ17U/h42TFCPHUIXH4EsqUU0liqWw+eQu1ToxE8er2eDRs2MGfOHLKzswkODqZXr15s3ryZBg0a1OSp/nEIKhWBffpy/tefa/U8sixX6kZZE0iSBFYr+Q0a0Pmpp655bPz0Z4mvwJzDgc/+/Rt2lZoFT49j8+bN5O7qyvdvPAqUDoAE6N69e5mBlDabjTfeeIO5c+dy3333VeLKLiNcGcl7Dax5hWgteRBZtTbkAUbnMqY7xIePQQOyTIFElTOlPvzwQ4YPH45KpSI8PJw573xMduoGEEU2//0Fg/t2xEfdlWzzbHJzjlXKPrXaC9FeWnzkF1jJzjMTFVb9IG5ZlvGzyWT6lrxhVjV1XRAEbrnlFrZs2UJ8fDzr1q2jRYsWtG7dmtTUVNd4URR55JFHSpzT19eX0NBQjh49Stu49uQfMlFotnNL40BueaQnv59I46mFexj/8VY+uf8mBjQM4IWdZ8g4foK0tDSXsCkPs8nKv88kEyuI/DExHr2m8reWTu1CWdAutNT2vLWJpKw+UMYIheuZaouP4hHzYWFhJTIhFCrPyZMnmT17tqswVPPmzTl48CAAoocHsqmo1JiUlBTef/99VqxYwalTp/Dx8aFXr17MmjWL6Oho13E7duzg2Wef5cCBAy5xePPNN/Pqq6+WKA5W12wfPwGfvXvJq2EbJnlnMa+wAfmZOeWWir8W7733Hvfeey9eZaQ/v/fee3zyySd0796dd955B09Pz1LHOANOK263qFE7q6xaq7ZU6e/hfOLOsdS9+FCJIjo75AuOKmdKjRo1ilGjRpXcGOeMM1vZ7/9cm1b8+Rk2qXI1O3Qab0RbEQ5JRnVxLUySJOa+vgWvbDuWhh7c/a82hASVX430x9+OER7uRXynkr+3RXkWPBDQXzFHdVLX33rrLe6++27y8/OJjo7myy+/LGWPLMtlBuH6+fmRlZVFUIgnHIKTSXm0j3EuFd4a04B2T/Si/7yNPPzFDhx6FUKOlbTFL+ER0KDMOL2y+G1fEqeNIiuaNayS8LgWsl1CUNX1I5FCbaPkLtUzDh06xJ9//kmXLl2QJMnV7AvAdPwYaj+fUmMqGjCWnZ1N8+bNeeCBBwgODub06dPMnDmTHTt2sGPHDmfgbyWe0GsKISMDSRAIe+XlGp03w+gHhfD8e7/Rs33pgm0VISkpiRUrVrBq1apS0f+PPPKI68l1+vTpvPLKKyVKyV9ConKeD7VBh9k/GmnD30DpJaDy8DM6vQ+5Vvf0qtE7ZPKog3NLvjgqIT4kSUawe6ATzeSarPh7Oj1Ev/x5Ct9sB/nBOvRnC/n2xS3o2/kx8Z7WeBjKrkSaklZI6rIkUoG13xwlpH0gt90aQ4CfgaSEXAxAQHhpsVpVQda4cWM2bdp0zesrHrNxJRdSC3h09VEA1pzIcIkPgChvA5/c05GHv96FUaPCeOE3zp3dR/yM/5QoLHctThSa8ZVkWoeV/nyqNnYJ1Eqw6Y2GIj7qGcOHD+e2224DYOLEia7gRkdBIalr1xB178RSY3r06FGhgLFbbrmFW265nC3Tp08fIiMjueWWW9i1axedoqNRn0nA127j7PLlOOx2ZLsDyWFHdjiQ7Q5kyYFstyM7JOTMDIQDB5HbtUNWq51FwS7eZGVJdhYE4+L/MugaNSR6yBBEUUSlUrn+l1Uq8lu3Iq6MYM3q8PSEHiycs411Jj1jy1lvvxp79+7l8OHDNGrUCLvdTnp6OkOGDGHp0qUEB19uyX7//fczefJk1/dLlixh6tSp2G02Rltl+gf3LzHv1ap7umzPTSVpzzq2nErCu0k4U6dOZeXKlQDExsby5ZdfYjSW/XR+yfuQK9R9qi2AQYKCOmjSJ+KHA2cTRJu1CFNhDubCXMymXCxm58tmzcNqzSfpsMyFQ61Bbol/q0HYbrICOnLyLJz66xx2XzVPv9SN5AuFLPz8INKebOYe+JvIPmGMGxlbKmvjyHHnedUd/JHP5FG4JZ2vt6RjCdMTGWSgpywT3tC31t+D4vj5+ZGbWzKTJzfXwtnzqSR62ujdVsObY9vRMq70EnjfCH+OP3Mzn376KQ999SGhY6ZBVNsKn7tDkDfzsixsP3iBLq1LL51UB6fnQxEfNxqK+KhnXK0oVP7yv5CtVvxGjiy1ryxXa0UDxi4FXFqtVhLuvJOI9AwiANP6v685TqBYLaPt2695bHFOvvwKgiwjShKiLOMQRTwEAasjpMJzVISUpHSOHk4AIFfnWe56+9UYOnQoKSkpgLOw1qhRo1xPqikpKYSGOj9of/vtN+IuNuyz2+2u6p5fP/Iy8//6ih4dS9aVvFp1T3AGt3o2jEA4dYTUVZvxbjKal156ydXmfurUqcyfP5+nrhEfY3DI5OOetu4GSaBArv1zq4QA8NjGypWxiKqrCy3JriX16LsgO/+2Mg+OYPH/jmAvsGPNtqBzwPD7WyOKIhFhXkx7Lp4Dh9L5f/bOOzyKqovD78y29B6SkEINCb333juCCgqoIKKCWBBsoJ8FFUQsiA1QBBUVRUF6kQ7Se+8kpBJSN23rzPfHwpKQHhISZN7nCezM3LlzZzaZOXPuOb+zdvEZ0jbFMX13PB2HhtKl7a0YnMgI20N+5Mh6uDhruXw1lTUrLsJZPSmxRraKEL7iCk4P1MS5GFM4ZUF4eDhnz561L6/ffImpq4+QkZbEE40a8sP/eqDSFPwQX758OePHj2fatGms9etKWlrB035nz57lhRdeYPfu3bi6ujLisceQez3CsbQsWhewT3Gnh69fv84HH3zA3r17OXr0KBpRzdH3VpT4eihUbhTj4x7AkpxMzKKFeDZtiqaYcRHnz58vMGDMarVitVq5fPkyr732Gs2aNaNDhw5cMhgRnJxwnDEdjYcHgkqFqFbf+lGpEG58FlQqm3aFZEV9o60gCLbaMKJo/yze+JwdG8vVxYtRyzKyqEJWiejPnEVw0OG6aTMaU9nFJ8iyzMBZ/5DoYHMBD9NeL9Z8++jRo9mwYQNJSUkEBQXx+eefM3To0AKP89prr3H06FEEQaBOnTrMnz8fyC0mZbWKNPXwIT3oViZBYeqeN4NbP/1oFk9s/gdLqi3F1O2Gmq0syxgMhiKr6DpaYbNopMfaYxiRMQEmAcJNIt92qYOH+53V2ygMJwQy78L0Xd1Gz3DxTBXUGhe0Glc0Ojd0Ojd0ju44OHrg4OSBk4s7Gp0D55dtASDdX4dLvIG0y+mYNAJmnUj9joHUreOVq++G9X1pOMOXf7ZEcGjFFU79eJ496yIYOqYBtat7kBiXAWpwuaGjUjPEgxdeaIHFIrFx1QUy9iZw8GAChw4mEOTrQN1OgdToGoyqHKcP+vbty/Tp07lwIYbPll1kXUoGvpf3oFKJzHh3bKGGx7Zt2xg+fDhPP/00//vf/zjy+yHiLqcx4JcDrB7ZMlfblJQUunXrRmhoKMuWLbPriVgvRJE28f0Cj1Hc6eGYmBiWLFlCq1ataNGiBccOHQXF8/GfQzE+KjmyycyZRx/Fasim1sd54wny3UeWefHFF6latWq+AWOdO3e2zx+3aNGCtWvX2qZsZAm1tzfVepd9yXGnwEDCX389323nWrZCpSnDKp+yTKZKx2inJMYObEZgE1uad1Hz7Ytu6IwURPXq1XPpf/z888/5tssZ3OoYFEiNAxairlzJdzvYgl+3b98O3Apu9fLzQRZVGCKu2tu9+OKLLF26lLCwMD755JNCxzpQ48zuzEy0SLjKoBUEVILADlcr/bacZkn7MEKqlC4OpiicBYH0u+D58A+pj39I/RLt89rbbUtUUr5nt+p07RjML7+fIWv3NdZ8dAihjitSVBZWj7y/s2q1SL8hYTAkjPTYDE6tusz5U8ls+OsSjn9fpnn1y4R2qINji7YIZRyY+cwzzzBr1uc07d4Xn7bD6O0Df+z4gXHjxuUKKL+9oOCZM2cYPHgwoaGhPP744+zdu5eRntkcSL/EkX/judK/PjU8bnlv5s6di16vz6MnMm78c2wbOpZXqZPv+Io7PdyoUSN7Fs8777zD0QOHURUSz6Jwb6KYk5UYS2IixugoRK2Wer8uwaGYaW/vvvsumzdv5qeffso3YGzBggXs3buXxYsXYzQa6dGjB3q93hanURFvGLIMosDq1asJCwsjNDSU77//Pk+zmx6F2rVrM23aNPv6zZs307RpUxo3bkyvXr1ITkpCEkSq+LgT1LRBkV6C8qS6o4yAjFCMm+fN4NZRo0ZhTE0HWULjfctjMmfOHGJiYmjatClLliwptK9pvcPY9GAz1j7YlOUPNeX3B5vw65DG/FQrhGtOAv32n+NYVOqdnl6+OIsiWRUlk1sEJTE8bqLWqBj1WAOemt4Oax1XOJ+OiwkatC/cC+la1YU2zzbisdmdGPREOCE+ehpnTsJpwwCkD2phmDWU7D++xnz5cmlPx05UVBoTvz2G44PTcNNqSFg5nb///JKxY8fy2Wef5Wp7e0HBffv2kZaWxsmTJ2nfvj1t27bl4d7diP7mJdL+XcIDi/ZjzRH4vm7dOnr06MGy+GzC1h5myr5LDBs2DGSJPcd3k23OP97Hw8MjT1BsftPDub4jq4wMuObQ41H4b6AYH5UQyWAgadEiUvfsQdRqCf/lF7RBgUXvCHz33XdMmzaNefPm0b1793zbhIWF0bp1a0aOHMk///zDhQsXbFMGsoxQArnyskKWZSwyTJo0iS1btnDkyBFmzZpFUlLuTIabcRLnzp1j7dq1drXdiRMnsmTJEo4dO0azZs2YN38+VkFErKA6NTnFoiRRRbzFStUcno6CxKRyBrd26tSJC0YDL2//O1ffoigyfPhw/vrrr1KNrVsNb1Y2roUgwNCTl7mSUPZ1g1xUKrIr4Z1Frb2zQXl5ODJpUiv6vN6cdhMaMKhv8fRjRVEkuF1V6tU4gkXWkt3tRyzVHkZlisTx9FQ0PzXFPL0Dpq2/I5egVtFNZFnmiXl72ZmRzbcPdiP24iGysrO4du0as2bNylPUc9u2bbn0bEaPHm1Xor79Z/p336GPz+StHRft7c+ePUtAzdq8HX2NNEeRJUmpeHh44OXrhzEqgpORycUee2HTwwCy1Wb0ODUsmcS7QuWnEt4i7l8kg4HkH3/kRO9eXP7iC5yCg9EGBqJyL176Ws6AsZslzovCz8+PoKAgmwtWkhBylL8urSdi9OjR1KxZkyZNmtCkSRMuXboE2ESn6tatS6NGjRgyZAh6vS2eAVnmRLreHifh4uJiFwG7Sc44CZVKZY+TAPJUQQ0ICEASRbuOw90mZ3CrRZDZlZlJnz597NtziklZrVaWLFnCwIED7cGtly9eYkG1MGq7efHPzm0AXLhwwb7/ypUrCQ8PL/X46vu7saJVHWTgyX0XcqVzFxdJlrmWbWJ/QhrLL1/nm5PRvH3gCuN3neO42YihEt1Z0q7bRMU8/com8LNODQ+aNiy5aKJj5GquO7TDsdNgdGM+RfPmfuSJFzC2/AxZEtBufwbrh00wrZ2LbM6/EOftyEYjF758gwXii7zhsoVB/fKf8igtL7aqjn+IG79tvsSPp2K4kpZFYnIKvyQbkWWZBtmQ7ahi09UkjA7OqNLSqO5VvOm8oqaHAax6m8qvNihv2rLCvY0ykVYJkLKzSVnyO9GLfsCk11Oldx8Cxo/H6Z13IJ8aI/lxe8BYcYmKiiIyMpKaNWvC7t0Yz1/g8sBBhCxfZs/YcHd3p3nz5nnkyAvL2JgzZw4DBgzIdazmzZvz3HPP4ejoyNSpU/nkk09sRossc81kKnEV1JtxEjeroGq1WmrVqsUXn33Ge+9uRlVB2R45g1vTExJ5rErVYotJAaRfjsEh6zqqHFlML774ItHR0QiCQIMGDZg7d+4djbGmrwsz/P14PvU6b++6RPsQT64bzCQazCSZzKSYrKSZLRyKSSXdKlHVx4kUFVgkGZNaQFIL+SunSTIatUToNQuyJCNUkAF4E1mW+e09WzaWs0fFFbCULBbcpUtkV3s013rBowq6/k8h9xuD8d/NsPNzdPtfx3pgFpawJ5GcaoA+DiEjDrLiEY0JCObriFIKIpkImAmVRQRR4kHrcuDTMh/7L480pcdnO3jn56NIDT2RAVkj8JSbO5ObhlBvx0meOBtJtghtVVp8vYpn5N2cHl6/fn2BeiLijVpBliQDau+S1fFRqNwoxkcFImVlkfLbEqJ+XIg5IwO/3r1xG/0km06cgAMHiIyMRK/X8+effwK2QFFfX99iBYzdxNfXl1q1bO7hcePG4ePjQ4sWLXB3d+fcuXN8+umn+Pn58dRTT2FMSUW/fj3GCxdyZWxAXjnywjI2CqJLly72zy1btrR7LmwxH6V/Vc5ZBfXF51/kgcFPQJNRpe4yy2Bmy9arGA0WzBYJSQarLCNLMpJse+uXZRlJlrHEZCNdzYRQV2SVgHxzu+TPuGcWk3XiBPWTIlj78o9898ZH9sC/gsSkAFQ6DYEaLX+9Md2+bt26daU7mUJ4uGkgL6+O43vnTL6/ctv0iywjWGTw1IJFIl0AV1nAQaUiUFThrVJTxUFDFUctAU5agpx1BLs44O+k5de/zqLfE0/mgyZcXCu2YvXqr49jtdiM0N5PNyiidflhSkvDQZAQ3Xzy3S4IAroOPZDbd8d45CDSps9xOPM5gmBBRkQSvJDUvkjaKkiurbA6+iLrXBG0Dgj12nJp5WREZO6kiEW6wcy/FxPZH5HMmbh0IpMyScwwYbLcMuLF2Czc3N0Z6KDig7a1Afi4WgDvXYxFm6anSc2STQ8vWLCgwOlhANFJjQCYYjJwqFNwwUmFew/F+KgApMxMUn77jagff8ScmYF/3374jR+PNiiIiIiIPOmdN5e3bt1Kly5dCgwYS0tLo3379rn2HTVqlD2Lo1WrVsyfP5+vv/4ao9FISEgI/fr1Y+rUqTaPxozpWNP1ZGzaXKQceWGeCIBXXnmFN998k379+vHBBx/kKcr2448/3nK1yjJ+Dg5sLoMqqNX9a/Pdut34NYHQ6iXXDpEkiVkf7sXneglTf8+lkyXKyHbxE9sHtXMICSoPpEwXxBXHCezUqMiunAK8kREwxiWUcPQlx+9oKs5eOp7uHkoVRw1+jlr8nbX4OmhKFZwJ4OqqQw8kpRjKxfiQJAlDlpH0tCzMJgtVq/nmO9aEqDSunrTFDY3/ugtiBaZrWk22aRRBXXiBNEEQ0DVrCc1+RUpJANGC4OqHSlRxezTWka3L8NjxNlVPvU89wcJe34LTwnMSn5bNjguJHI5M4Wx8OjEp2aRkmbBIt2KkBMBZp6aalxN1/FxpFuJJxzo+1PZ1ocuqemRGR9jbPh4WgOsfp3kwJZHmTQt++bhJSaaHb6bspx+LxbVzUIV70hTKDsX4uItImZkk//or0T/+iDk7i4C+/akyblyuYNLq1avfKGtfMDnr6YAtxmL06NFFHn/MmDFFx4KUgT7DjBkz8Pf3x2g0MmrUKObOnZtL/fOLL75AkiQeeeQR+zEbeXkxtQyqoF6KPI/GK4hl/QNo1rHZ7UMrkl9/P4vPdTOeXfzp3qs6WrWI+kb8iEolIAogCjceYjfug8XRbvjt6V8xGIp3bUWtFpOjJ8QXv6JoaXE1SPinSjwWVnYib+7uWmKA5FQj1ULybjcaTeiTM4i+GkdWuhGVrCEr3Ygh04Qh04zJYMVksGAxSliMMlazjGQG2SqCVQRJRMgRriarTaidJSQzSBbB1saqsrdp0a96hRoeAA6e7lhlFeak4n+nomfhfgzzqVXUkKPYH/QEOt9aNOr5VK7t5+P17LyYxNa9R1j/3XRSrpxE0DriXL8bHp0eR1BpUAng5qghzN+VOn6uNA3xoGOoD9W9nXNlic2ePZuwTi/Tv39/u55IamqqXeBwz76NCKKYS0E5P0ozPSyIAgkRl3HfG4xLu4qrQaVQtijGx13AmpFJ8uLFRC/+CUt2NgH9B+D37LNoAovnoryr3Ag+LG35b8Cu+ung4MATTzzB0qVL7e1WrVrFTz/9lMtLAqBRq8qkCqq3uxfubZ8m7lpumeniYDRZiNsZh8VXSzWXy3RqOwRJknj99dcZO3ZsrrYFyaMPHz6c06dPY7Va6dixI19//TWiKPLnkV/498IR/rfjY1q2bMn8+fMLrcVhcfNBuBZf4nMoKQ4qEYOlbKXQPd1t3o5dvx7lwO8WrCZsxoPlhlEg559RJSMjixYElYSgkhE0MiqNgNZZQK0T0Tqo0Dqq0TlqcHDW4OCk5fimWFRaUGsF1G4iGgcVWgcVOicNVw6morY60XpQTeCW5H1Jv9OOHTvaA5pjYmIYOXIks2fPZtGiRbz22mv23/spU6bcMqhvQ+XgQpKqDqroPXd+gW8QPnw617/chse1vfgPeRMxMxFJBaKDK8vmTKZF4go+00/h3ILX0HhWpfaId3Cx6jm17GvqV3flp+/n4VMMz1R8fDzvvfeevTL5uHHj+PLLLxk8eDBTp05l/74zzNv0DQN7DCuxnshNck4PA/ap5tOnT2OVJbZfOsqZwybaB3bJpYaqcA8jVzLS0tJkQE5LS6voodwxlvR0OeGbb+TD7drKB5o1laPfeUc2xcZW9LAK5eqz4+TTYeGy2WyWa9euLUdHR8vp6elynTp15MTExFxtmzdvLh87dky2WCxy69at5ePHj8uyLMuxN87RarXKzzzzjPzxxx/LsizLBw8elOvUqSNHRUXl6ud0g4bypcFDymT8ZoNRrvHqCnneF3+UeN8/V5yTv3p2s7z7QJQcGhpa6Lm3aNEi33O/+XsrSZL88MMPy8uWLZNlWZZ/eHOW/MXTG+WLy3fKI0aMkBcsWFDoWA4OmyAfatlNliSpxOdREga/t1nu/fY/ZdpnbGKG/OqEjfKsF9fIc6eskb9/d53800cb5SVfbJb//n6HvP63vfKO1UfkzSv2yAd2npCjr1yT01IyZIvFUmZjsFqt8lfPbpa/fHG1LMuybDabS/2d5qR9+/by1q1bZVmW5YULF8qTJ08u9piiv3pezvhfDVmyWkt/Yrdxeu86WX7HrcCf0X0ay87OznJSUpJ9n3nz5skqlUqOiYkp1jEef/xx+YknnpA7d+4s9+/f33bc06fl7t27y46OjrKHq5c8ptWjckZaRq79OnfuLFerVs2+vHDhQhlbkec8P6NGjcq1b0HtFi5cWKrrpHB3KMnzW/F8lAPW9HSSfvqJ6F9/QTIYqDroAXyffRaNf9nWLykXbsYq3EH575EjR5KYmIgkSbRp04YXX3wRgNdffx29Xm/Pgmnfvj1ff/31DX2RsnGLq3VavCzZxCdnlXjfcwevgQPIpqulDra9KYNutVoxGo121/Wo9ybx87hlHFgRS/NmzXJ5jfLDY0A/TB9u5vTMRdR/48kSn0txcdSoSDWVbQE6L3cH/nAxMa6JE88+2rVM+y4uJ/bbUpND29uCFMsigDomJoYrV67QqVOnUo1JE9YJ5+s/kXbhNO5hZRP8Wrd1H44kfUb29csILv4YM5LpcuWWqNixi/H06NHDrkQKMGzYMMaNG8fGjRuLnK7dtWsXf//9N+fOncuVDlu3bl02bdoEwJqZu/HKsOLsljtjpbTTw0CuqefIL/5F6+pIwJiST6MqVF4qUTb+vY9ktXJqyS8c7NqFyIU/4N+nH43XrqPqO+/cG4YH5EqfHDRoEOfPn+fixYs888wzgE2O/PaMjUuXLvHuu+/a99uyZQvHjx/n5MmTfP/99+h0Ntfupk2biIuL4+jRoxw9etRmeMAdZ7vcThWMxGeU/IFqTjPj4ONQqmDbnNsffvhh/Pz8cHFxscu5iyqR1n2DSdBU5Ycv5xY5N15jZB+ym3SHHz/hzNzlJT6X4uKgVmG4MdVWWl0Xg8HA6NGjCQsLo27duhzYuwc1EnpDxVTVtVis7Ps7Alm00ONBW5mzO/1OAZYuXcpDDz2UK7j1t99+o1GjRowYMcIuCV4QHi26s/KclWZdu5f4Gg8fPpzGjRvToEEDxo8fb9dmOXbsGOPem89zn6/hlc+WENLxcXg3Dd5NIwsHrlxLz6MJ4+HhQUBAQK4idPlhtVp5/vnnefPNN+1TqbcTfTWNsBQLqf7lkwYrZZnJikrEraES6/FfQzE+ygCLycSRDWuYP2EMm//4GbXZbCuU1rE9mhxl1+8JytAIKA6yLNviTMowir2KFnZLboyc/AOjXvmBp179gXGvL+D5N37g/Q9+zldUKzJaj48RHMtAC+LPP/8kLi4OWZbZvHmzfX3YkFas3zsbf8eaNG9QeFaAKIo0+flzTKEtsM55h9QT5+94XPnhpFVhkmR7Jd7SKMx+8MEH1KlTh3PnznH8+HEaNGiATqw44+OHKVswp2qo3cENlbrsFHv/+OOPXDEdAwcO5PLlyxw/fpxWrVrx/PPPF7q/6OLGpI0Wlj7VpsTXeN68eRw7dowTJ06QmJjIihW2Kq9vvfUW06ZN4+jRozz++OPMnDnT3pdGNpOeZci36rWnpyfJyYUrkX7zzTdkZmYWWj355K+nMAjQ4bHySWM2XEgBGSXN9j+IYnzcAabsLPav+JP5z41myw9zCQyvx6OffUPtP//E6qAjccLzXF76e0UPs2Tc5TooV59+GmQZbfUaZdbn8PY1aSxmAjIGCVItEGsSWY0fCzK8SI5PzLPP8r/OAVCtnnehwbRQeLDtTbRaLUOGDLE/JMAmhpblqGdQx1c4/cu2Is9D1GgIW/gVkqgm5ofy+T1y0qowynKuaYmSKswuXryYSZMmAaDRaPDw8MBRJZNhLNtA1uJizLih6zH8VnH3O/1Or169SnR0NO3atbOv8/b2tnv1nn76aQ4cOFDouPbv30/tQH9CVedLfI0Lms7Lqe6blpZm91DERV9BI5T++ickJPD222/z2Wef5ZFnv8nG30/RINVKcnNfXMupSnLW6US0VVxQuVesXoxC2aPEfJSC7HQ9h9au4Mi6VZiNRup37karBx7GM+CW27be6tWcGvowWW+/x5lr16j7/IsVOOLic7eLsGXtslXXDXj3nTLrs/fADvQe2CHP+sXzV/DWZTUuHm55tsmSTJYK+nWrjsUSZJdHL0nar9lsJjY2lmrVqmG1Wlm9erU9Q2jNmjV8//33bNu2jT8mbiY5vnjqq07ermTXaolu745SXo0i+tepMSOXWtclNTUVtVrNK6+8wu7du2ncuDFz5szBUQ0ZpooxPnzrqkk6DQlxifhVtdUEySl5X5Lv9CZLly5l6NChuf4+4uPj8b8xnfr3339Tv37hFXZjY2Pxr1oNV45i1qeUWDvn4YcfZuvWrfTu3ds+nffxxx/Tq1cvJk6ciIuLC/v329Rcr2z+Hm9ZjaenF2lpeTO/UlJScsWB3M7bb79No0aN6NixI6mpqYCtcq3FYiE1NRUTGjyOJLIZK48/GFboeZcWWZJJOhWBX/vSlxJQqLwoxkcJyEhO4sDKvzi+aT0yMo169KXlwAdx9c6rWuhYxY/Ga9dzbNhQHL/6liNxsTT5YEaFVlgtFndxfLFvvgWAY7NmiI7lL51syDYCanSO+b9F3QxxK22wbVZWFo8++igZGRnIskyXLl0YN24cAC+99BJms5lOnTqRHJVGz8Zt6PL2Q8Uat1uP7pjm7+HgX9sQ/Kogy1Zq1AnGYDCSnWnEkG2w/W8wYjQYMRpMqDVqej7QPl/xLYvFijHbhCHbgNaajYClVPVdbH1ZuHTpEn379uWrr75i6tSpfPTRRzg5tSTTXDGF/XwDPEg6nUZcZJLd+LiTAGqwTbnMmTMn13Fmz57N6tWrUalU+Pn5MW/evCLHpvawpaumnSzcS5Iff/75JyaTiVGjRrF582Z69uzJN998w9y5c+nXrx9ff/01kyZNYs6cLwi98gvHvXtTt250ntiOtLQ04uLiCq0PdPbsWXbs2IGnZ97pDk9PT2ZO/4ER1EbsX6PUYnRFYY7JQMo24xiuVLT9L6IYH8UgIzmJfX//wfHNG1BpNDQfOIRmfQfh5FZ4wTeNszPNVqzk2JOjcPhrBQejo2m2YCEqteYujbzykvr336TdqMwa9MXsu3JMo9GERtLlawDevmrQoEH2t8ubrF271v45P3l0Jycn9uzJX8fhptYBwIIxf1G9avG9AtWG9+HryP1kntgGJ26s3FzIDjfYd2QnKkGDjBUJKzISMhLcVu7+YQcRd4/2pdJ18fb2xs3Njf79+wMwZMgQ3n33XVy6tOZ6VsV4PiJOJAIadA65pwtK853eZN++fXnWffTRR3z00UfFHlfVqlWJi42CcDBbhBJp59wk53Rez549WbJkid0oGjZsGF9//TUXN/1AAzmVzF6v0jdzfR5BsKVLlyIWIQg2e/Zsu8fjJhMnTsTR0ZEZM2aQtMvEdVGmefvgYp9/STGcS0bQqdCG5PVUKtz7KMZHIeQ0OtQaLW2GPEKzfoPQORWvaiOAqFbT5KfFnJz6Bs7LV3Kof38aLVmCg2fBLs+KRC4DhdOiuDJ8OIYjRwFwbNUKte/dKZdtMprRFlCyXODueXwEQYASlE538nHDS6sjExg+ZAQxUbYgRQdHnf3H0dkBJ2cHnFwcUavU/L5oFWaLEZVKjVqtQqPRoFZr0GjUaLS2/7VaLcdjYkiOOU6d+g1KNS0hCAK9evViz549tG3blm3btlG3bl0SdSqu6ivG+Mi+LiC6ZlK7fvk9GEtDyxYtOHPqGCda1KVa49ase/r5O57O8/LyYu/evbRp04bNmzdTs0Z1qhyezQGHtrQKa8K4cdVzCYLFxMTw6quvMm7cuEIFwZo0aZJn/B4eHri4uNClSxcOb9xBfBWHcpU7Tz0RjVudqgiqSu4tVigVivGRD2VhdOREEAQazpjJhRo1cJz9JSf79KHmwh/wqldxha4KpJyND1NUlN3wAKj+04/lerycGExmtIVWur170wSytWTTHL2HDGbBqlWcW/sXg6ZMKbL96OcfLla/aTuPkhxznCyjudTTEjNnzuTxxx8nPT2datWq8eOPP/LOXwfJtpawPk5ZIQhYs1WIqrLLdCkLkndvYnYvgcF/xCOsbFYm03nz5s2zp966u7szsZM7zmRR47EvEQQBT09PNm/ezAsvvMDgwYNxdXVl7NixfPjhh7nGdnu9qKIwCiCW45+LNcNEZkwiIR3blN9BFCoUxfjIwe3TK3dqdNxO6DPjiA+tQ/zEl4l8dARpH7yHd+06ONeohcqxfKLFS0753VEkq5XLNwTGAFz79Subfk0mfn32DzJVHohICLKEgIwo3PgfGVGQccCHPlpXZry2HUSQBcGW7yUICHozLlb4caotAPb2y2DMtmA2WHFw0aBS26rXCkCHR+pQu1nJaokKUGIjL6hlS9ps386ejAyC/viDZsOGlWj/gnC5Ef+SkW0s9bREzZo1+ffff3Ot83DUYJRuxQKUVtrcYDAwbtw49uzZgyiKfPfdd3To0IHvv/+emTNncvHiRdLT03FxcbH3JUgqBEmFxWRB5Vh4Ibe7hSzJSNs/oVPdMC7+ciiXqN6dTOd17tyZI0eOAHB41zoa/TOCQ8FjaB1c294mpyBYQdwuCFZUm0wvHe6JhiL3KS3GC6m2FNswJcX2v4pifHC7p6PsjY6c+HfthvOKv7kwciSG16Zyc4bX7cvZBPbsXebHKzHl6PmQ09ORjSYAXHr2JOizT8ukX3NSCmm6qgRq4vH0UiFZJWSrhNUiI0kSkhUkSUaSNKQ7uGDVCCCDIMlgwWasqAQEjYDZkP9UgcVk81RYzRLIIpJVwmSwcvVkUomNDxkZSvFW3vPll4l/63+sOX4ck9FI68ceu+MAZqcbRm9GtvGO+rkddyctZlQYjCbUKpFJkyaxdetW3N3dad68OUOGDLFVUr7BTX2L+vXr0759e4YMGULDhg3tGiKLFi3CbDaTmZkJQOvWrdm4cSNdu+ZWUD2254L9s9ah8sRWRa9dSrB8iJSO35SZmu/taHZ9QoSmJs0fe79c+s+Jys8Jn+tl+zuTk8yziWj8XVC5Vg7jUaHsua+Nj7tpdOTEtXoNGm38h9PT3sFw+AiuUbHoX5iI1/ZtaJ2dkQ0G1D55M2juCiWcDihR11k2yXPB0RGf5ycU0br4mPS2fkPbh1D/0bwptuXBmd1xbPnpDFXreJR8Z1nGnJXJ1TMHMGanY87KwJSdiTkrA4shC0t2FlZDFpbsbNKzzFgFL3SCFovFgkmtxqoSWX/pEiemTOHpEgQ85ofrDc9HZnbZvsV6udiMmuupGURdOltqafPFixfbszVuaogAuTJRbvLrV+tIOWk7H686dz9tvCAsWel4HPgfibrm+HQdUT4HkWVqGk6xN+BxauucyucYOTCkGckop8srSzLJpyLw66ik2P6XuS+Nj/KeXikOamdnGs38BIDDox7Dcd8hLnbtikqSEQBNYCBeY8bgNbKcbla3IWVnEzv1TTK2bi23Y0Q/ZzM45OxsHMPuTBsgfv9Zjvx+GGSZTIMI+KFzvntCRBqd7e1VlkruKTKJDginj5M5xOb5EQDdjZ+cWES40rYNW7Ov88+GDSBJdGrXjoYtWwLg6+oK2LwGf/75J8HBwRw8eNC+f0EVdm9OfaxYsQIJaNq4MeHtytbr5nnT+EjLLHMNEdcb552T5Gt6u+Ex9O0mVKlaeQK64xe+hT9JSA/+Wa6p7HrRDYf0yHLrPyeqFCNpjuUTU2OKTr+RYltBL2AKd4X7yvjITE1h77Ildk9H6yHDaN7vgbtqdORHsx8Xc2HxT1yZ+w1B2SYcnV0xx8Zy7f33cagbjlOzsi+oJMsystVKwscfk/nvv5guXc61Peq5CSBJyFYLssUKFguyJNnWZaWgdTWj8XQCWbJN1cjYPgvCjXgJ4cbPrfAJ49lL9v7Tvn7L1lYQQABBEG2fxRv/31jWVA3BsffIPOM/s/IolzP9cTMnAOAjxODXpE+ZX6eC0Ohsfzqm7JJldGQbDFjVTpzv6IV308lonFzRODmhc3RF6+SKzskVnbMrjk7uaLWO/D73f6x/+xtOHD9hn7KY/9NPuaYsRowYwZgxY3j22WdzHWvevHm4ubkhyzLDhg1jxYoVDBkyhB9++AG9Xs/58+dJTs/io/ffJfvGdFhZ4eNqe/tO1GeWuo+CNERuD5YE+O0dm9HV4qGASmF4yFYLWVFXiNi4mfCEn7kWMp6q4YVL6t8RgkBsjYdpevF70lKScPcsP20Ms9GCf6aVa1XLx8NiOJuM6KBGG5zXyFT473BfGR9r5szi2qULlcboyIlTSAhnAn2o+8Qz1Og/CFNUFJd69uLqU2MJP3K4TI5hzcjg6pinMBw/nu9231deQb92LcbTp8nYssW28uabWs7/rVaMKhmVk8pmXtx8mbO/1eX2BtxcrfUQMaXapnViv/yreIMWZMJ2dUb0Dsq12mKWcDIn8/iCR4vXTxmTmWab75asEss/PUSnR8PwDnQpYi84FW2Tcfds2ZiWfYcU2f5a9DV8qvgUOGUBturAERERefYtSJJ73rx5LF9uK1bn5uSAo6MTBmPZzt/7uNv+tpLSs6mWj37FnWiI3M6W5Te8PYJE6551y/Q8CkOyWDi7dCVH9loQseDmlIWrixVHUc+F2ABSzFVp5HQOlZsV//blPx3o3eJBnC59zekze3BvN6DoHUrJnr/PUU0GdZc7T2XOMlqIuZbB9fh0MhKzsSZlE3DhOqrq3uWaxqtQ8dxXxkeWPo3w9p1p9/DdmcooETef0Dee29pg2x+2nJ1N1AsvEvzlnAJ2LBhZlm21H7Zu49qHH2KOjrZvE52ckLKy0IWH4zvpZVxvlAn3GftUkf1e7d8SUasiaPneko9JkpAz9TYviSSBLCHbP8u29VYrsiyTsex74r5ahpSRmsf4sFokVIWmzZYvksV27N3LbN6cHb+fZ8ikoj1UMcnxgAatU/Fc1lkZWbmmGfKrtloY+UlyR0VF8f3337Nq1SqqVatGcI1aeJax56OKh834SMk0MrRj6aTNC9IQuZ2LO/XoRBfCexYu+leWxO7cwc5lV0nMrkot38s4uahJ12uISXQlNbsGwR5XadkqA1ddC+SDPyL+ORomnwXH8sveyM601XjROJXfdbCYJdyOJXHGXU2fhsUrmplltBAVp+d6XAYZ1zKxJhlwSDPhkW7BJ1vCEQgBzAIkOImc1BrQNqlDOfqJFCoB95XxUanJZy64zsEDnG/RkozNxZCzzEHkqNFkHToEt+ftiyLeY5/Ce/x4VHdBzjw/BFFEcPUoVlu1r61IliU2GtG3JoKjo/3t3WKREe+gcNadUqeNP4c3RmIxSWSnm4m6eJ3RD77Bmm0/2bQYWgyhdcMeSGoLT77RjSpePuzfv5/Jj71A2rVMehl78khnmzEwYsQIDh06hEajYeDAgcyYMQOAzz77jLW/rsWYZaRXr178/PPPJR5nfpLcGRkZ+Pr6cujQIebNm8ecr79hVIO2ZXp9PJwdAZmUDOMdSZvnpyECNu/N+++/T3x8PB/88RRNQjqz+I25ZXoO+aG/fJHdi7ZzKaEGVZzgodFO+LfJnTZ80+i3U80R/nwSFvSC50suq15cjLu+Igl3gus0LbdjnD4ch48kIHQLybU+I8tERGQaaRlG0q9lYk3KRptiwlNvxjdLwhlwBrJUcN1VTZa7hsQAJ/Q+jrj7OuPn70KIrzOXUjP4YMd+VtUrWQaZwr2HYnxUOm5NWahcXHDt24f0deu5PHgI1RYtRJVPeWzJbCZr7z4S580l++ChfHut9sfvONavj1DJhJcKQ3S3zd1fefJWSW9BDaJawFjzCVQe/hU1NLQ6NU982B6AT19cQZqYzJpdPzJ+4hQcnZz4fNb/qN++AVXiw1m5Zif9+rTjmWefZeYnnxCxTmDOupc5ceIEDRs25IknnuCXX37BYrHQo0cPtmzZQrdu3Rg7diwx5hjWLFrDwIEDmT59OoIg5JqyKNZYb5PkDgwM5MEHHwTgwQcf5M3/vY3JXLaCYCqViE6Q0GfbPCplqSEC8Oyzz9pjXL57cz2mJC0B1conziHlwkX2LNrJlaRqqDChU3nSvZuesAeHI6jz/j3lybJp8CBc2AjHfoNVE2Hg7DIfY3JCDOEp2zlQ/Vk6OhU9/Vcask1WTsXE0xGIPR1P3IkEdHoz7ukWqmRLeAAegEmABBcV6R4aEmu7ofd1xNPPhcBAV6p6OlKnkFTjbYmp+Dtoqe2kVLH9r6MYH5WF26ZdbuL/7ruYLl3GePYsF7p1J3DWxzh37YooisiyTNxb/yPt77/BessLoPL1pcayvzAcO4Z+40YCPvqojIs/3Z25WMcewwiZmYU1MQ45Iw0pMx05KxMpOxN1qgbRuYIUNG/DrDOSYo6kQ/s2THvbVr34WvRJ/KuqMCYY4V93Fm7cTlJUBvFrXXAAGoa3taeT9uljC5TVaDQ0adLEPq3i5uZGWMMwFiUsIjo6GrPZzObNm3NNWRQ4pkIkuQcNGsS2bdsYPnw427Ztw8vXD4u5bKddAHSihN5QfNXM0hLSyJ2LW7M5ezSCuk1rlFm/p39bhpOvF1f2R3AlqRoATepeo9mTg9EWUdcpD4O/hat74NBCqN0T6vYvs3ECXN61lKZYCO/zbNGNiyAlI4sLcWeJSTxPiv4iJkMkGjkaD108VR1EJPFjal82EO/iQpabhmtVnYjVm3Co4kTd1kFUq+JMTVXp7jf7rlylZfVqlSZNWqH8UIyPykIBf2xqd3dqrlxB1HMTyNiyhegJz9vW+/piSUy0xUmIIk4tW2I4ewaPYY/g9+orAGh69MC1R4/yGe9dqAEjqNU4PzA2322qqYtQU3HTLrkQZNJSUvKki2bq0xkzqw37jh3n4L8x+F3wpu5oJzzcXXE91Iv9e3MXK0tPT2fNmjW88sor9nWb/96MwWDg888/JzAwkClTpuSZshg9ejQbNmwgKSmJoKAgPv/8c/r371+gJPeUKVMYPnw4M2bMwMvLiy59HsBSxp4PAI0IBnP5f0ddB7fgwq5N7F1zrkyNj63bPQAJCKFR8DnavDgSjWu30nUmCDB2M3xWD/4cDRNPgmvxYiaKg3h5Cxc0dQgPCCm6MWCxSkQlRhF57QyJqRfJyLyMbLmKoxiDly4RlSjhBjiIOjJ0AciqYLSObdF51uag7nX0qgBGd1lVZuMHiMw2EpVt4GWfuxe7o1Bx3FfGx71hTOf/UPd/602ioqIwXroEkoSUnY2malUcGjWi6syPELV3UQlQlm2psBWI1SrgUFk8s2LBBfm8nD3p264zvlpn9m3fTLc2tloVl06eztVOlmVGjx7N+PHjCQ6+lUUwfMxw6latiwkTGlnDM888A+Sesli0aFG+xy5IktvLy4sNGzbYl1+d8SXWEtT1KC4aUcBoLn/Ph1anwdVPjT7BhMViQa0u3W0t9ewZzqzbR0SEFhELUBUQUQlmmozohcb1DqurOvvAIz/Dr8NgXid46h/wLJ6xUBiZmZnUTj/A2RpP5Nmmz0zlYtxZYpPOkaa/hNkUgUaOwV0bj6PaJiznLguALwZVIGjbYXGtha9XHWoG1MXNuWoeL8SC5FVoyuFeui05HY0s0cFTSbG9H7ivjA+4IW1dKcl/2uUmmqpVqblq5d0bTlFUsCFntQqU8hlT9ogyri4exMScsK8qabn0119/HU9PTyZPnpyra2dHW9ZIt37dmDhmIu+9916ZDVu2yhhTs3CQzJgNsHdPFFaTFcksYTVLSBYrGVnpHBevkaixYLZYMUsyZquE2SrjoHKgtp+O1GwD+mwz6UYzGUYLWUYJg1kgyeSOj6H0Oh8lwclbICPakY1/7KPfiPYl3v/YD0v4d783WtGbmlWvo9Jq8JGu03xoOzxqVSu7gdbpDe1fgn+/gC8a2ZYHzwWn0mfBnN+3jqZCFik+nizdPgtD9hUE61WcVXG4aVMBcAQsVlcyCUBS18ag64mTe21CfMOp5heKRl08S95gMeJrPk22Z9nUFsrJ9th4wn19cMsnjkbhv0dluX0r3BNemZvIVOSArdkG4jKC8PSMqLAx5EQQZYICavLPP7+VOJ0UYO7cuRw5ciSXNwPgwoULuDjYggc3rNtAeHjZyU1LVokvP/ycFCkdFVDd6k3Qioh82zbDjQ4kgighCBKCYEWy2LwAm7AAagTRiqgSUKlAo5ZRq814aq/jw/UyG3Nh55IUYQZ0+AWXXGBs96eLOHIhhCa1I2n93COoncpZnrznNAjrDyueg/PrYVZNaDgMBnwO2pIfW6uzqck2ifiUw2HemK3+mORAMlWNUTvVxN87jFoBdfFyufOA3GPx+3AmA3+/sp3ONUkSx+ISeKJR/TLtV6Hyohgft1FU9c2CpKwLqr55k1deeYVFixaRmJhY6PELct9XKip4iNkJcQB4BVQSkTiVjEjp00mff/55atSoQcsbsukvvfQSTz75JDNnzmT7zu2kp6UTEhrCst+WldmQEy7HkSKlU9erJvUaNkDj7AVuLqjVIhqdGo1OhUan4vCyf6h2zoPdL7WgasCt6SBZljmTEIebgw4fZ1cc1Hmn/Wb9MYvUcxllNuaC2LrsCNY0HT71oHnHokXGZIsVWZIwpaWw4+vVXIivjq9jDO1febLcx2onpDW8cAgu/AOrXoLjS+DkX9ByLPT6AFTFvzXXbdOXtMOBeKTF0qvrSVTl6BI8n7ALD5zo4Fe2qssH07LItEp08VamXO4XFOMjBxaLpcjqmwVJWRdUfRPg9OnTxMfHF3rsjNQUAPQ3/lcoGMuNImg+tSou1TYXahnZWvp0UksB8Rbff/89ey/sZf0v62nYsyFBQUH5tisNsmQTSTuTfJn0PRn0HTqAwNC8tTQ0NVzgHFy7GpXL+BAEgXp+VfO0z4m3hzeZ1kzSs9JxdSq/h8qF/QngIDPshb75breaLZgzsxFVIge//4sj54LQCploVCYyLdXxd4mm7+Se5Ta+QgntCZNOw/E/YP0bsO9bOLQIOk6Cjq/Yyg0ApqgdGA7OAbdAXDvNRNA42LvIvvw37km2KT1j2jlU3uXnPZAyDpOobYROVbYVg7clpeGpUdPQpWL0hxTuPhUbNVjJ2L9/v736pouLi13KOift27fPZYzcZPHixUyaNAnIXX0T4LXXXmP69OmFHlu68TCQrJUkg6MoKjB612KwGR9qB4ciWtpYvXo1YWFhhIaG8v333+fZPmHCBPz8/GjRokWu9SNGjCAsLIwGDRowZcqUAvsXVCBYy+dP6VK0TUHV36tsDa2AsGDGDHuCFsENSTXp+WHxIi4cOpunnYuXBwD67VElPkZVX5txcjHu4h2NtShUDiCZRJIT0vJsk81GVr75GwveOMB3r+7jyDmbAWeSnfFwSufR57wY8tEInAICynWMRdJoGLx6Cfp+DKIKtn6IPLMaqUsHkDk7BO2CgTie2oTrzkWYPg3mt1fqE+qvpbavhsWvPk6miyMp7Yejca8F3LqX1a5dm2nTptkPYzAYGD16NGFhYdStW5ddu3YBNs9sWFgYDRs2ZMyYMQUaxKjdUFn1ZX76eyKu0iwkCPHeyApQKAPuL+OjiF/soqpvFkTO6pvNmjXjySefJD3dJnX8+++/06JFC0JCCo9qd/e2vXV6eFZ8UawiyUoGfWyBm4t62Jf0xng7FoOtDomqGOkuN71ZW7Zs4ciRI8yaNYukpKRcbUaMGJEn3gLgiSee4OzZsxw5coTdu3ez5Wa9m9sQ1IC1fG6aNzMN6lStU+Z9h9SryYCnHuL5yS8iInJ6/7E8bcIbNMaKFa2p5EGAtQNqA3Dl2pU7HmthuHjqECUtVy/k9S6e+GkpsfpAgtyj6NoxmeHjXAn2iKRxrUgGTX8M70ZNECtL5LIgQOtn4Y0o6PwGFtmA26mdZIlZJPecAK9cRP/wx1hNFt5edJb1Tzqz/a2azNovkDrodzx7zkVU2wzyCRMm8Ntvv3Hu3DnWrl3LiRO2YOibHtpz585x/PhxGjRoAEDv3r05deoUx48fx2g08tNPP+U7RJWoRS2ll+lpXzeZuZCeSdcq98C9T6HMuL+MD5ly0afIWX3z8OHDBAQE8NFHH5GZmcmcOXN4/fXXi+xDuOFeLc7wSvtw/+eff2jSpAn169fn5ZdvqYYeOXKE1q1b06BBA0aMGIG5CM2HwoZYnId9SW+Mt2M1Ft/zcSferD59+iAIQh7xr9sRVEA5eT5k4cbVLsc4m/joeMyCBf+qed/+RVFEhQqP7JJPm9SoUgMJibjEuLIYZoHcrCzcuG1uA+3QwhXsPFCV6l4R9HtnGPVGPoxXk5YM+uhJOrz6ZOUxOm5HFKHrFKRnt7KtgzepdVvh1X46Ggcf3Bs8y+Guy2nQcRC1ZqQQ+NI5Bgx7mh2Hbr0MxMbGYrFYaNSoESqVikcffZTVq1cDBXtoe/bsiVqtRhAEWrRoked3/eY957VHlnNw9WkSjbmzmAq653Ts2JEmTZrQpEkTfH19mThxIgARERF06dKFhg0b0qtPX6yZmXT2UuI97ifuL+OjCIpKhyyI/KpvHj16lMuXL3Px4kXq1q1L9erVSUlJoVGjRkX0VnixtNI+3CVJYuzYsfz999+cOnWKjIwM+0N47NixzJkzh5MnT1K/fn0WLlxY+BAdPRHc878uRT3sS3NjzHMNDDbjSF2M+jSl9Wbl5Kb4V5cuXfLdLqrLb9rlZmq4IJfOs3Ji8yFWfvsHK7/9g1Vz/2D9gr8xZhpytTl70Gb81W2TfykvCQlXa8mzMFSiCrPaTHpG2b4p5+R6bCIZUTavzHcTNrDxf/PZ981v/P36j+zd50qT6ufo+/5oNE73XizBlYMvoJKgeuuvc62Pv5ZY6O90Qb/zhXlob2KxWPj111/p1atXrnU37znLNv/I0j9S2Xsxd6Xtgl4odu7cydGjRzl69ChhYWEMHjwYgMmTJzN+/HhOnDiBT4++sHY5vtqyjSNRqNwoxkcOWrW6VX0zIyODdevW0bt37yL3y1l9E7BX32zYsCHXrl0jIiKCiIgIPD09OV5AOXtBuOn5KPwVt7QP98TERFxcXKhevToA3bp1Y9kyW/bE1atXad26dZ71paGoh/2d3BhvYrlRgbU4xsedUpD4V05UahFBKp8/JWPmjSmmUtbk2blnJyevnefy9UiOx51jb9RRZnz8EV+9/xlfvf8ZmxatRrCAI1rc/PLXmoj0uAbAvu1bOXv2LD179sTZ2Rl/f39ee+01TKaCpdllrUxCbAKvvfYaTZo0wdXVlaCgIEaMGEFkZGS++6xZs4Z27drh7OyMp6cnXbt2JTpHReacnFr/j/2zBR3xya4cPO6HbJVoVDOSNi+MRCyl1HdFkhG5hhh1JDUcu6FxKxvV1oI8tDl59dVXadOmjf1+ALnvOTX86tGqlSM7/rmlblrYC8VNYmJiuHLlCp1uVM8+c+YM3bp1Q5JlkoKqkbov/ylWhf8uldTvWDEUp/pmflLWQ4cOLbD6ZrERi/dmW5qH+/bt2/H19SUzM5MTJ05Qr149Vq5cSUaGLQ2yVq1abNiwgd69e7N8+fISewbKgpw3xq+++oqpU6fy0Ucf8eGHH+ZtazQDjqiKoceQnzerJIXZChL/yolKIyJaCzYOouMiOXXlGHpDGnpjOnqTHr1JT4YlgyxrFkbJhFEyIksSAiKiICAg8midRxDVtgeng654wbW3I8kyYd41eOiFkVgtVjYuWkG2wYAsw4mk8+yKsKWLO8kFx8/4DawLP6cSf/YqQ4aPJDQ0lGXLlhETE8OkSZPIysriq6++yndfUSsSdTaKI5uOMGbMGNq0aUNiYiLvv/++3dj39fW1t1+8eDFPPfUUkydP5sMPPyQ9PZ2dO3diMBjy7b9aoJ5Wvq8jj9uMQ5UaqNQqTBkZaIuQQS8qpX7//v08+eSTGI1GnnjiCd5++20ARo8ezY4dO3Bzs+mc/PXXX9SqVYuXX36ZrVu3ApCWloa7uztHjx4tdAwFIUsSF05NxVFQEdRxdp7tRf1OF+TBzc9D++6779rbffPNN5w5cyaP4ZDznqLTuOPtoyYxruh7Tk6WLl3KQw89ZK8x1ahRI5YtW0br4Y8Ts2s7WQnXint5FP4j3FfGR3GKFRWVLlmQlHVB1TdzUpTGBwBS+UzuC4LA4sWLGTduHBaLhY4dO3Lxoi0L4YcffuDFF1/kzTffpG/fvsV4yxYKjE0prxtjTqymG9MuDkUbHzm9WfmJfxVGQeJft6PWiTga3diw418SE9LIMmRjMVmxmCWsZplz189xtso+El2i0UkaXCRnXGQnXHDGSXBEJ+pwEZ0QNSISMjIyh8zH2HxlEw39bdofM2fMREK6EQMi4yw7okKFKAiIsoCIaLuxC6AVNTw0cig+1f1zlXdXqVX0HfugfdwdLsZwZt8JLOlGPLMK9iKlJiXhhIo9v+5Gr9ezfPlyvLxswYEWi4XnnnuOqVOn5jtFqdFp8A/y5+zZs7lkz9u1a0dISAg//fST3bBLTk5mwoQJzJ49m/Hjx9vb3v73mBPBmo2TKg2Luzcqja1/rWvhsQPFSam/OY1Qv3592rdvz5AhQ+y6LHPmzGHAgAG5+vz888/tn996661SS7wDJJ2YTbJDBo28nkHU5q1QW9TvdEGCdjk9tG3btrV7aMHmbfr+++/Ztm1boWOPSD0PgLO2ZMGhf/zxB7NmzbIvf/rppzz33HO8/+VXiPWb4OpcSTR7FO4a95XxUbkpnuejtA93gA4dOtgNpMWLF9sfSvXq1WPTpk0A7Nq1i7Nn86Zc5hppIUMtjxvj7ZiNVlSYEIrhTr8Tb1ZB4l+34+zqgB64+KsRCS1WlYBVtCCpLEgqK/Wy2tFEXZ9RTw5A5+CAUAwv12MLh2GRLTzQ7gFWCivJMmZhspiITYjmXNYFRmmHoVPrkCSZ7Og0ZEniSOJZflj2MyarmUPHDzOs30MkSXqCRdv3n9/bvF/twFsCeWEv5BLIe+WVV1i1ahVarZZqal8SrifSrFEju+EBMGzYMMaNG8fGjRsZPXp0nvNwcHTAQe2Q54EWFBSEr68vsbG3AiX/+OMPrFYrTz31VJHXx44pCwCVQ/EfXjmnEQD71OXw4cOB3NMIgH0a4abxURRLly7l77//Lv455EAyZ3Eh9ls8ZSd8Gr2ab5vi/E4XJGhXkIf2pZdewmw226dFhg4dyptvvgnkvqecu7aT64kS/dvdEhkrKlbu6tWrREdH065dO/u6wMBAVqxYwdAdB1Gnp3PxcuH3HIX/HveX8SHflWKspaR4Ayvtwx0gISGBKlWqkJGRwZdffmm/8Vy/fh1fX18sFgszZ87kxRdfLMZw8x9vedwYb8dqsqAWi18CvrTerAK1Dm7jkQF92F/jON5e7tQIDMJBnXuK5KcXtxGoqY5DCYIe1aiJMcexee8aPNSOPNJhGFqdjrU7l7P68lo6DOiGt3cVAGJn7MOSbWLqlpns2rWL5T8uZea8z/CrVhWtkw4/P1v11ILe5gsSyOvduzcfffQRKpWKns07cPr6RQa26JNrnB4eHgQEBBRosLq6u2KMMPLr1l8xWAxcTr1MXHocIeYQEhISiMuK48eNPyJJEj//9TM+VX344psv+Orzr4iJiaFBgwbMmDGDvn3zFxDDnI2EgKgqfmHF0k5d3uSVV17hzTffpF+/fnzwwQe5PIVHjx5Fp9MVaDgXRcy+SWRprTSo+b49Ay4/SitoV5CH9qYXND9atWrFseNHmLvpA3yNi9lzEL6ff+vYhd1zwGaMDR06NJfnOTExEbWbO6eTU/Fb9ou9YKLC/cP9ZXz8B7iTh/uMGTNYv349AFOnTrXXCvnpp5+YP38+siwzduxYevYsQu2xiBf3sr4x3o7FLKEWyr9aanHRajV0aNq88EYlTFapqvNnlfkf3omaAcBXF7+lriaUw+YTuAsuuLjcqrAqiAJHok9Tv359atWpzSsfTiE68xr1mjYo1tv84sWL7cbD7emXN6kTUJOtR/dQMzyv3oinpyfJycn5nkfLBi1Zd2wd57fb3PVq1ATJQfz8y8+4urpS3bM6V3bbdECuXblGXFQc77/7Pl/N+YqAgAC+/vprBg0axNGjR6lfP69yp2wxYEGN9i6JU82YMQN/f3+MRiOjRo1i7ty5TJgwwb79jz/+4JFHHilV3+b0KK5kbiSAIFxrDC6jEd85MemXePJJAx+MnoYZR6ZOfb/Y9xywXZM5c+bk6nPz5s1Mmvom17MNtBv6cMm8XQr/CRTjo9JQeFXbnJT24f7555/nmpu+yeTJkwsNqMyXCvQgWcxWVGLlMT6K4ujlPbz521wcvtcWu17QB49+wqOnTzL6idFcjrmEoW4m0mMSAbIvo0KeolOnzhiNRpydnfm0/SvEp5c8/XL79u25sox2795N48aNmTNnDq454iYsFgvbIw8hIJAen7+RURCtQ1tjeNhAsG8w/u7+aNVapk2bRmRkJGvXraVVx1aoBTWiIPLvv/9y6dIlHnn+EZ54wlYevkuXLtSpU4eZM2fmK3wlW8xIJUzau5Opy4AbSqgODg488cQTLF26NFfff/zxh93ALykRe8chiVCr6ddFN76LbD0xlXbtnBnw9Dqa+jW1ry/OPQdg3759edY98sgjHGjQmuMxMXzXq+RViBXufe69HLT/KveSqnAFSyBbzDJq1b1hfFgsFn7d9Q2fPPNtiRRWRVFk2rvTmDZtGqnX0mjs04wXq0zit9F/8e1X85k2bRpHjx7l8ccf5+vt+atRFnd8xUm/7NSxMx6ObiTr89YeSklJyRUHcjudG3Smpl9NnBycWLhwIe+//z7z5s2jZ4+euOvccdY646hxtPdRvW51+74ajYZOnToV+GCTLEYkoWRpyEWl1OecRrBarSxZsoSBAwcCEBdnE0yTJImVK1fm8sYcOnQId3d3ateuXaLxAGTF7SJKOEM1dUt0PsWLLblbVLWcIMrrxVyGx50iyzIHIiNpXS3/9HWF/z73l+fjHnjAC0KlDUq5jYobp9UsoxbvjRo4+/fvJ9C7Or4eVXLpstycDgGbwmpERESu/WRZZvfu3fY368cee4zVa1YzcNBABEGwa6CkpaVRxdUHf1cf1sfcqrJc1umXX38xm8OrdxMRczXXONPS0oiLi7NP4RXG8uXLGT9+PNOmTWPMmDF5tt98kKcnpDP7r9kICAiCwJmrZ4i9HsuXy78EwZa5dTN+oHFiFP4lfIe6k6nLkSNHkpiYiCRJtGnTJld81B9//MGwYcNKNJabRB5/C1kj4OHXvVT7lxdZZgMy4KwuXap3QVzIMnLNaKarj0eZ9qtw73B/GR+VmuJPu1QKKnLaxQJqVeFKsJWF2NhYPJ29ufn9FldhNSkpCS8vL/tDNud+H3/8Mb169WLixIm4uLiwcvi36CwqTu78pFzSL6e+8DzGn67QtWYbvjzwM6mpqfa4kKVLlyKKYi5FzPzYtm0bw4cP5+mnny4w1XnAgAG88847RJ2IwsliS6O2WqycP3qekJAQrh23aUEIOf5WGmDAKMjkTUgtnNJOXRZU3wdsAdOlxS/oUVKvfsrh+Jl4R3xDjdqv4l5nZKn7Ky43C1qKBQS3rjn/E15YCa3SOt/tpWVrsh6tKNDGo6TfnMJ/hfvQ+KicT/dSqmdXDELFXkWLRUCtvjeMj5uU5UzVN998w9y5c+nXrx9ff/017y6azax+r5db+uWbUz4AZFq0aIq7hweDBw9m6tSpxMTE8OqrrzJu3LhcqZXdu3cnMjLSnkFx5swZBg8eTGhoKI8//jh79+61t/X19aVWLVsl1mbNmvHQQw+xY/MO+vXsZw84NRqM/LL4l3xTXQ8vHkD21XtjCq4wvOqPo034GK4deZ8rxt85GP02rpenE1j7eQJrjy+6gxJy4cIMZCxkZV0lKWkLtWu9QbVqT9u3my1mfj46nUD9L0Q7D6J7lSZlevwdUfE09PXF6R5Un1UoG+5D46OyUzmNo9xUcMyHVUCtuheuk83jkJJ5S1yuuAqr3t7eJCcn20XCcgY9LlmyhDlz5iBJEt2tjfjiik28qbzSL/e/t5y0qiZ6Pv0IZ86c4YUXXmDw4MG4uroyduzYPCq0Vqs1V5ryvn37SEtLIy0tjfbtcwcXjho1Kleq848//siUKVN444030Ov1NG/enE2bNhWosSFIFqzCf+MBJqi0+Ld4H8fTNTh95QPSHQ2cvfoJF2O/o2bNlwkOerxMjmOxpHM1KndByouXPiIxcQtNmixCpdKx8coKqul/Is51KMObvl8mx71JtlXi5PXrjG9auWJbFO4uivFRWZBv+7+yU4GCKRaLiE53b8R8tGrVipikCK7rE+zBjcVRWBUEgTZt2rBmzRoGDBjAL7/8Ys8A8fLyYu/evTSr1oDNmzZT0ysEtU/51bkxayyIJtv3XbduXbsgXUFs27Yt1/Lo0aPzFSDLD2dnZ+bMmZMnNbMgBMmKpYIDoMuClNPzuRgxG0GGNAcDziodjYNeI0WMJzr6Z86ff5fLlz+jZo2JBAePuqNjRUbOB8Dffwgq0YkqVXoRETmPlJTd7NrVmqZNFxMTvwpPMYSRLWYUSxm6JOxJzcAoyXT1diu6scJ/lv/GK0Nxqcw3qUo8tNsRBCo25sMqUlmrod+OWq3mkfbjmPztOJo0acLkyZPt0yE31T1Hjx5N27ZtOX78OEFBQfYg05kzZ/LOO+9Qq1YtPD097UGh8+bNY/z48bTs056fjizn7UEv4vdC2WUi3I5ZbaUEmm53FfE/4Pkwp13m5NWZSFhxEN0Jc3mAVj2P41PnSUJrT6FzpxOEhDyDJBk5f2Ea23c05erVRaU+Xvy1lYiClvr1PiE8fBpeXh1o1vRn6oT+D4s1kwMHH6CWeReOvg+WueEBsC0xjSo6DXWcCq4npPDf5x65hf/3sVezvSeMkIodpFVSodbcO/P8TWu0Z0i3vnSZfEuIrDgKq6GhoRw6dCjP+s6dO3PkyBEAot/YWe5fh1Ur45lWOQMD7/Vpl+sXFnD+8kwkUaZxiz9w8Gmcp40oqgit/Tq1ar7C5cufERW9kAsX3+fKldl4+3TFybEajk41cHGug7NzLUSxYLVXkzkVgyEaT8+2ebYFB4/Gy6sje/f1QkKgS+3yCXjdFxFJy2oh5WLYKNw7KMZHZeFG1Lkglq50+l1FgIp0fVisKtQa5cYFILpqkK3l+114pjpTxeBRrscoLaJkxXoPPsQMqWc5f+Bprqti8TZqCGvwRb6GR05EUUXt2q9Ss+Ykrlz5nKtRi7h2bWV+LRFFHWq1M2q1O1qtLw46fxwdg8nIsCnZhgQ/nc9+4OhYjSycSXIdhIuuZMXjikOUwcTVLAMv+HqWed8K9xb3nfEhV9biLnbj4x55i6vIaRdJg1pjrLgBVCJEZy2W61nleowUnyyqRHvmqpBbWRAkK+Z75W8GkCwmog+8zOWM9aisMg0c+lOl36cI6uLXphFFFbVqvUKtWq9gsRjIzLpAVuYFsrKukG2IxmiIw2ROwmxOw2CIISvrCiDl2N8BH5/O+fZ9KeUcTmTi4NvlDs80f7Yl6xEF6OhZOT1pCneP+874qKzIN4wPilHxtMKp6JgPWY1ae+88cGyUz/eq9tRhic9EMlsRNeXkNXNUEeeQRFAlMzwARNmKfI9Mu6Rd/ouz594iQ2skyBxErbYLUHuG3lGfarUD7m4NcXcrPHPEYjGQlXWRzMzzOLsUXPTu36vrCAam7PmYFdeuMa3pw3hpyy4241ymAX+tBneN8ui537k3/mrvB6y27I17YtqlolNtJS2qe+3mVU6XTFvdljFgvJhaPgcAREc1jpbiv5nfVWSQK3mglDn9Kmc39uDglVcRJImWge8Q1m/HHRseJUGtdsDNrQEBAQ/i5lqw8eFgshUAFM2xbD/7EZ2W9mXMv98Rm51ZJuNo6OJErNFMqvneidlSKB/uK+NDqMQ3KVm+t6ZdKmr2SpYkrGjR6O4FI638cWroA4DhTMkKvpUE0VGNs8WxUk5ZysiV9u9atlqJ3/c6e3Z3JZ7L1NF0pWXvY7iF31mqbHkiGyJJsDqw7/ETzO39G8Ee9dh/8Ut6/tWH4du+4HJGaqn73nP9EnP2v4lL8s98dOjbshu0wj3JHT3pPvroIwRBYOLEiQAkJyfzwgsvEBYWhqOjIyEhIbz44oukpaWVxVj/08jWm9Mu98BDtQIDTq3ZtvgGla6SvonnQ3leKVOc7XqIruV4PRxENKixmirf22q2xoHAbD2yObuih5KLrIh1HFnfmFOZf+Ip+dK22QqCOy1AKKJGytmzZ+nZsyfOzs74+/vz2muvYTIVnuccFxfHa6+9RpMmTXB1dSUoKIgRI0YQGRmZq92BAwfo2bMn/v7+6HQ6QkJCeOqpp+wp3wBmcyqyyg1BEGjv34B1/b/ilwHLCfVpw4nIhQxc3ochm2ZwJi2hxNdk6eXtpKX+i4UjHDj6W4n3V/hvUWrj48CBA8ybN49GjRrZ18XGxhIbG8snn3zCyZMnWbRoEevXr+epp54qk8H+p5FsjyjhHoj5qMgRWrJsD1u19h6bdiknMnZFA+DaoWoRLUuPqLYZxJYiHoIVgdzmOXzMRs7s+KCihwKAOS2O82v7sffiBLJVRhr7TaJh373FqlSbkpJCt27dMJlMLFu2jOnTpzN//nwmTZpU6H6HDh1i2bJlDBs2jBUrVvDZZ59x4sQJWrVqxfXr13P1Hx4ezhdffMGGDRt499132bx5M3369MFotAVwC1IWoso5V/+NvWvxd59Z/D14DY0CenMh9k+GrujH4I3vcCo1luKiN2WA6MTopk/h5qAIjN3vlOoOnpGRwciRI/nuu+/44INbf/QNGjTgr7/+si/XqlWLDz/8kMceewyLxYL6XlGGqgikG4qdqnvA8wEVFnBqyba94aodKtbzIcuyberJ/r9tCsD+WZZvrLMtY7FizTTf0rkThFtWnHDjH+HWppzLt2+/WdkVwHRFj6ARER015XauqhveOItc+VRlmzccyal/3qHKnvkcyE5FRqbVgG/u/kAkicvLPmXf8eVU6XIN2ehA3aZL8fKvX+wu5s6di16vZ/ny5Xh52dJcLRYLzz33HFOnTs1VPycnHTp04OzZs7nur+3atSMkJISffvqJyZMnA9CrV69cBQC7dOlCcHAwvXr14tChQ7Rr1w5ZdMBq0ed7nNrugfzW4z2uZjzP1APfcTTqbx5ZsZJg3270qTmIgUFNqOniXuD5pZv0iKITmeZMHDXlp8ircG9QKmtgwoQJ9O/fnx49euQyPvIjLS0NNze3Ag0Po9Fot7oB9Pr8f/HLAkElcvbfHVw5fODGilt3e/u8sZB7U+73/FtPXPv0942Hjn2lLN9qJcu55sllcj6QcuyDjGSx3dgvT51K0rvvgSiCINqyXwThxrKAcHOdKCAKN9oIAkhWW8aMxWqLH7FakSX51nprjkJsgv2fHOsE+xnmSmrIsXDzkzU9E6cqJlKn+iMLKhBVCIJoyzq4cS1l++UUbvUtCzkepLmepHmvdX6ZFYJAuskNyfoyK5eC4/pVuQyAm9fX/vC/deHz+5jj+uetKHzzW5SRQRLs31neuAeBPDvnQ9bVVM6/HZfvtjv2JJnh7GurbX7MnEZNrut547/iHOy2Nu5mMxnmbF5cMpRsVSEpzvLNa3bb6vw+5byOQsHfl21RLnhZlgkRdLybrab27j8B0AtOuPX92PY3cxdIPbGVrXM/5nKiihC/Kmj09TA47ODgkSE4WHvRutt0dE5Fv+mvW7eOHj162A0PgGHDhjFu3Dg2btxYoET9zQrDOQkKCsLX1zfXlEp+eHt7A2AymbBKVtzkVIyOzQrdJ8TFl8VdpxKTOZ4ph37k6NVlfH99I9/tExC1gfi61qGudz3a+jWkV9VG+DrY0mozzZmoVE5kmjJwVCnGx/1OiY2PJUuWcPjwYQ4cOFBk28TERN5//32eeeaZAtvMmDGD9957r6TDKBWdHxtD9OmTOW50co7AyVvGQM51ebUNcjyMc77F3lh/861VyPEQuPkAztVPjmVBEGyBlGfOUruHv203SQZZshkOspx7+fbPkgQqFYJKtP0vqkAlIqhuGAYq0RZLUtBzUpbz/0z+6+XMJJyryjjW8APJArIEVjM3nvq5rmXRn287Vn7Hz/F9ecjQ8VIWpiqtEdQa+/W7mW0pCLeufWEP2gL1Km6zf+zfkSjkXifk/K5v/goI9l+FnN+tlGakirsWnU5141TkW+edz7LdOC20jW3ZHJeBytMBUS3a+pZuv663FvKNF82n3e11hkySicMZx+hd0ya3fXuAZ1HaH0W1z7O9FP1vMWeTHnuITvoUAnf+AsGtodHQQve7U8yp19j/zescOJ6AkxYGjnyA0IFjEQSBtJRIDu2cjMlpHVv+2Yqv6yiadp6ESlXwLffs2bOMGTMm1zoPDw8CAgI4e/ZsicZ2/vx5EhISqFs3b2aL1WrFarVy+fJlXnvtNZo1a0aHDh24kLALV5WEdzE1PgKdPfmp00TM0gQOJJxjY+xRDl8/SUzaObYl7WD7eQszEFBpA/F2DSU54zIatQsGQxYOjs5FH0DhP02JjI+oqCheeukl/vnnHxwcCg+c0uv19O/fn3r16vHuu+8W2G7KlCm55jT1ej3BwcElGVaxCa7XkOB6lbiS4uCKHkDlRwCadKvoUdx/9KJ2RQ+haJoAadFcP9oS6+6vUTV8uFzqOclWKxeXzmLbmi1kmtW0aFGb1uM/ROPsam/j7lmNboP+JP7qbo4fmUoa81i/7A9qVn+N8BZD8zWoUlJS8vVieHp6kpxc/GwmWZZ58cUXqVq1KsOHD8+zvXPnzvYqxi1atGDt2rWo1Wouxq1GK0Pj4IeKfSwAjaihnX8D2vk3sK/LMpvYcu00O+OOcTrpNHH6C1hM8QR69CA7KxFHJ0Vk7H6nRMbHoUOHSEhIoFmzW245q9XKjh07+OqrrzAajahUKtLT0+nTpw+urq4sX74cjabg+WidTodOpxQYUlBQKAPcAvHtN5nEdZ/i80NvqPcAtJ1QZt0nHd3E1nmfEZksUiPAlYcn/A/P0CYFtvcPaYd/yDYunPiVy5mziNFP4crv82naagYBNVuW2bhycjOQdP369Tg75/UwLFiwgNTUVC5evMjMmTPp0aMH//77L5npJ0mzOuDmWOWOx+Ck0TIgqAkDgprY10mShCiKjFkyHC8vvzs+hsK9TYmMj+7du3PixIlc65588knCw8N5/fXXUalU6PV6evfujU6nY+XKlUV6SBQUFBTKDEGATq/gkRxJ/J5liBdOUsU3HGp3v6NuTcmx7PnyDQ6fTsTNQWbwqIeo1e/JYu8f2nAENesN5fi+mVx3X8yJC8M5dbgNrbt/gqunP2DzcOQnS5CSkpIrDqQwvvvuO6ZNm8aCBQvo3j3/cw4LCwOgdevW9OjRg2rVqjF//nyCml7DrPYu9jmVFPFGDE62JRtnreL5uN8pkfHh6upKgwYNcq1zdnbG29ubBg0aoNfr6dWrF1lZWSxevBi9Xm8PIPX19UV1r2RyKCgo3NOoH5iDf+unMf05Dv2qd3F7uXTGh2y1cva3GWxfvxOjRUXbtmG0ePYD1KWIWVCpNDRt9xZGw3Mc2P4q2e7b+XdXF9zUD9KqxzuEh4fnie1IS0sjLi6O8PDwIvtfvnw548ePZ9q0aXliRwrCz8+PoKAgLl68SGiTdGTHoo9zp2SYM3DTKam29ztlGg5++PBh9u3bx4kTJ6hduzYBAQH2n6ioqLI8lIKCgkLBCAIENELb5iksiZchM7HEXSQcWMfvzw1k7aq9BPq58uSMj2kz8fNSGR450Tl40aH3Apo3WYGaGmTrfmfjqtY0b+jPpk2bSE1NtbddunQpoijmSpHNj23btjF8+HCefvpp/ve//xV7LFFRUURGRrJuwyoeHXiJCYNWF0vYDOCxxx4jNDQUZ2dnPD096dSpExs3bszT7uTJkwwYMABfX188PDzYOX0/0Ueiiz1Ghf8mglzJNJP1ej3u7u72FF0FBQWFUpMWTfJHLfAaPgcaDSvWLtlxl/j367c4fkGPl5OVriMfo1qPx8ptiBFnV3Lu3DSySeSpJ+MIC2vAu+/NICYmhkmTJjFy5Ei++uore/vu3bsTGRnJxYsXAThz5gxt27YlODiYefPm2ac3wOZxrlWrFgDjxo3Dx8eHFi1a4O7uzrlz55g1axYRERGENwrm8UeNSLoxfPzu1zz22GO5jpkfw4YNo2vXroSGhmIwGFiwYAGrVq1i69atdOzYEbBlPNavX5+aNWsyZcoURJXI46+NIPOigUMHD9GwYSVOAFAoMSV6fsuVjLS0NBmQ09LSKnooCgoK9zpX98lJb/jJ8uGfi2xqNaTLx755Wf5qeG95zqN95INfvypbDFl3YZCybLVa5MM7P5Lnf1NDbtrUUdZpVbKvr4/8yiuvyEajMVfbzp07y9WqVbMvL1y48Gbuep6fUaNG2dstWLBAbt26tezh4SE7OjrKYWFhctu2bWUnJyf5cuQRef2mmvJ3W/rI8+bNk1UqlRwTE1Oic7BYLHJwcLD89NNP29f99ttvMiBfuXJFlmVZvp51XW71eSNZq9PK06ZNK/F1UqjclOT5rUiOKigo/HepUg8HrUDaP7NxbzwC0uMg6SJkJYLGCcL6giwTu/5btixdzrVMHfVr+9Dx+fdxDqhx14Ypiiqadnidus2eoU7wa5g025AlkfrhD6DV5lbz3bZtW67l0aNHFyhAlpMxY8bkiQXp1KkTPXv2pEZIE04m9CYkbQM+XYORJKlQYbP8UKlUeHh45JqyMZvNALi725RP9SY9okZAo9VUykKFCncPxfhQUFD476JzQazRgewzu1F9WBtrdgYWWcQiqdCpLGgeW8LO72ZyKlZNFXdHHp38AoGtelbYcB2cPOk84Ds2/z0cyfkAoli+t+icwmbtwqdwdP8GYjN2FFvYTJZlrFYraWlpLFy4kAsXLjBv3jz79gEDBuDn58fkyZP58MMPiUyN5OqKOERB5LHHym8qS6HyoxgfCgoK/2kc+n+Av/e3GK4eRa1zwb3NGMwrXyZVL7HgvU8BNVWqVmHkp98hVoKq0hGn/0FyPoDO3JmAam3K9Vg5hc12X/gKBxnqBz2Ap+eqYgmbLViwgKeffhoAFxcXfv/9d9q2bWvf7unpyc6dOxkwYIC9No3aWcXylX9Ts2bNsj8hhXsGxfhQUFD4b+MbBgNnk1NxSFN/CL6HFtLndBxbtx4nITaBbL0eZw/PChsmgMmYwZnzryCIzrTp/cVdPXZG5mVMkoYavsU3eAYPHkyTJk1ITExk6dKlDBs2jOXLl9O3b18AEhISGDJkCLVq1WL27NkcTDjI7Nmf8uQjT7Jjx4585d8V7g8U40NBQeH+QxCgxRjqtwDnRof4a8Y7HNmwmpD6jagaVg91IarM5cm/G8eidsokrMZ8tLryF+LKKWwmy1bkG3V1iits5uPjg4+PDwB9+vQhOTmZV1991W58fPzxx6SkpHDo0CF0Oh2pZ1Op+3QtEudk8v777/Prr7+W05kpVHbuTtlHBQUFhUpKSKMmNOs7iH3Lfmfp+29yckterYrVq1cTFhZGaGgo33//fZ7tEyZMwM/PjxYtWuRaf+nSJVq0aEHt2rUZN26cPcjy+++/JzQ0FEEQyMjIAODU/vlIzgdwtAwiJPTuFDDKKWwmCypUglwiYbPbad68uT0FGOD06dOEh4fbS2jojXrcdK40atSIS5culc1JKNyTKMaHgoLCfY0oqmgx6EH7sptv7tomFouFSZMmsWXLFo4cOcKsWbNISkrK1WbEiBGsXbs2T9+vv/467777LhcvXiQxMZE1a9YANmnzjRs3Uq1aNQCS4k8Tk/Qp5pRA2vWeVdanWCB9+/a1C5tZrNlIqIotbJYfu3btyhXLUa1aNc6cOYPBYABs2S5OKmeOHTtG9erVy+o0FO5BFONDQUHhvufwulVoHZ3oPX4iNZrm9l7s37+f+vXrExgYiIuLC3379s2j5Nm+fXu8vXPXRZFlmd27d9O/f3/Apgi6atUqABo2bEiNGrZUXovFyIE9TyJZRdp2/RnxLpahGDduHK6urgwePJhj++LYsiGNV199lXHjxtkDRMEmbFa79q3KxmvWrOGRRx7h559/Ztu2bSxbtoyHH36YDRs28Pbbb9vbjR07luvXr/PAAw+watUqDm87xP6vj3HhwgUmTCi7gn8K9x5KzIeCgsJ9T1JUJKbsLDZ8+wUJVy7RdfQz9rL3sbGxBAYG2tsGBgYSExNTdJ9JSXh5edn7KWi/Pf9MROOZSIjPDNy9qpXRGRUPT09PNm/ezAsvvMDXbx/F0VHg6bEv8+GHH+ZqZ7VasVgs9uVatWphNBp54403SExMxMfHh0aNGrFt2zY6d+5sb9e8eXM2bNjAtGnTGD16NJmmTDyDPVi7di2dOnW6a+epUPlQjA8FBYX7nm6jn+VsnbpkpCRzZP0qQho2oXaL1uV+XLMpE4vjLpyNvanTpHjy72VN3bp12bRpE/+c/BAx4Qc8arcvUtgsPDycv//+u1j9d+vWjW7dbDEsz/z5BB7efvTp2qcshq5wD6NMuygoKNz3ePgH0ObBR+jx1Hic3D2Iv3jevq1q1aq5PBYxMTG5piQKwtvbm+TkZHuQaX77WSx6ZItIWKNnyuhMSk9Y1YEAZGbHltsxMo0ZuDm6l1v/CvcOivGhoKCgkIOstFSiTh23L7dq1YqTJ08SExNDRkYG69ato3fv3kX2IwgCbdq0sQeZ/vLLLwwcODBXG43GA6tJw7GTj3Js95dleyIlJO2G0eHi4Fdux8g0Z+CmVQqGKijGh4KCgoKd8/t3AxB7/ox9nVqt5tNPP6Vr1640adKEyZMn4+3tTb9+/YiNtT2wR48eTdu2bTl+/DhBQUEsXboUgJkzZ/LOO+9Qq1YtPD097cGn8+bNIygoiPhrSUyYmMbcb7JJNMxm8/JhmAwZd/msbWQY4gBwdQwot2OkmzNw1ymeDwUl5kNBQUHBzrl/dwDQ9clnc60fNGgQgwYNyrUuZ2rtokWL8u0vNDSUQ4cO5Vn/7LPP8uyzt44hSRL7Nr9BhssyNq/rRJOm8wio3rK0p1EqLGY9AFpN+RgHFslCtjkbV61rufSvcG+heD4UFBQUbtD58TGIajWyVbqrxxVFkbY9PyY0+EsEtYkTZ0dyeMcnd3UMzjqbtHxqdny59J9hsnl0lGkXBVCMDwUFBQU7aq0OyWIhMTqyQo5fPbwvnbpsBkN1Uizf8s/ygWRnJhW9YxmQbTUBoBXK57GQbkoHUDwfCoBifCgoKCjYEUQRlUaLMTOzwsbg5OpHj0HrcROeQHY6y/Ytnbl6YX25H/d62kmsMtTx61gu/etNtmkdxfOhAIrxoaCgoGDH0cWVms1akpl6d7wNBSGKIi27vkOD0J+QTFrOR07g8K7p5XpMgykFgyyiEssnFNBufOgU40NBMT4UFBQUcqFxcMCUnV3RwwCgas22dO+7HXNKCAkpi5Gs1lzbZclC7MGpHFnXlLh9r9/RsYzmFMyUXzXfm8aHMu2iAIrxoaCgoGBHn3id09s3k3g10i4OVtHonFypXnM0GmcjEWfW2dennPyWvevDOaP/nWSdnoikv/LdP37/FBKOTsdiTCn0OJIlA6ugLbTNnZBuSkdAwEXjUm7HULh3UIwPBQUFBUCyWvluwpMA+NcOs9dkqQzUrPcgVqOKS+fnk3H5b2K3jOJwwicY1RLN/V6nilibLAeZS7tHY0w5m2vf8ym/cyJ5ATt3NufQ6rqcWt0ca3reGjOibMQq6MrtHPQmPU5aJ8RyCmhVuLdQdD4UFBQUsAWb+tcOI/7iOfo8N7Gih5Mbk5H0WCc8apxiX8RkAJyNaho1+Aan4O6kn04mIf4iEYadRBzZCbKMu0GHpy4UlSTinK3DQXAlQZdAmmBEt/cZgpt/jCHxGAb9GQyZEQSZr5EueJXbKeiNelzUitdDwYZifCgoKChgk0N/YPJUlrzzGhu+nc3w9z+pFN6P5OPbWPjhJ6h0AeiczFTx0FLdoye+XWYgiCoAguu9QXC9NzClnifp3PckJu/ALGQTYz2FWQdag0S93v8SbtZzedfjRKrOEHn0lmiayiLjYrLikF1+55tu1OOiVYwPBRuK8aGgoKBwAxcvb3qMncBf099m49w59Br3YoUaIKlndrPwQ5vYWMt6gbQbuhJBXXBQqNajDgGtP+amQLosSWTGbEKldkZQqVCpPKnW4nMcT89G5xSCg0ddHLybcnLXJzQ9vIgTfV4ut3NJz0jFyVnJdFGwoUy+KSgoKOSgeuNmdB39LCe3/cPFg3vL/Xhnz56lZ8+eODs74+/vz2uvvYbJZEI2ZLDgXVt67ZCHO9H+je9zGR7Vq1dHEIQ8PwaDwd5m85YtjH31B+q2HYmTkxP16tXji+/+xq/lbHybvI5r9UHExByi4b5FHAxsQINWL5bbeWZl6HF2Veq6KNhQPB8KCgoKt1G7ZWu2LprH2X93ENqybbkdJyUlhW7duhEaGsqyZcuIiYlh0qRJZGZm0t56FYDmjQKoOfS1fPd/+OGHmTx5cq51Ot2toNF58+aRlZXFtGnTCAkJYe/evbzzzjucPn2ahQsXApC+cSoxDs40Gv0Pglh+76MZpgyqOQSWW/8K9xaK8aGgoKBwG2qNFo2DA+f37CL10Sfw8C+fSq9z585Fr9ezfPlyvLxswZ4Wi4Xnxo/Du3836vur6Dx1foH7+/n50aZNmwK3f/vtt/j4+NiXu3TpgiRJvPXWW8yaNQtnNx21UmI51/ABTmzYwuTJk5Ekiddff52xY8fm6mvChAn8+eefBAcHc/DgQfv6ESNGcOjQIUSVSPNWDejZpznXr0aQfS2RXbtOcfhMFKIg0KZGMI0mBkO70l4thf8SivGhoKCgcBtO7h489cV3zH32cX54eRzPffcLDi5lHyy5bu1aunfuiC49muToo5j0KTQ0X0CSZOKSY3nv75N3FHOS0/C4SdOmTZFlmbi4OFxToqguyziEdGbSiEls3boVd3d3mjdvzpAhQ/D29rbvN2zYUDp1asH//vc+i//6lOT4GDITkxCzIhjRujqO2Srmrd2CS8RVAmv5cDE9natpmbwx/SkcHZ1IWHmQxk51S30uCv8tFONDQUFBIR+c3D2o27ErZ3Zu5dKhfdTv3L1Y+0kmI5dWfkN69HmMmZkYDQZMRhNGgxmjyYLRLNt+LCJHDuynZY1g5r42NVcfro46HGqEFzkN8ssvv/Ddd9+h0Wjo1KkTM2fOpGHDhoXus2vXLnQ6HTVq1CAq+gIAZ85GU79+fXyr+HD83F5qhldl0uujqFPDB3OKHiHNiEMG6NOzyU5M5tofWzFqJSyuasLrhnBeiKFuaDOaa7Q069qHZ54ex9ChQ/nu++l06NABWZb57J9B+Hj6F+saKvz3UYwPBQUFhXwQBIGezzzPmZ1buXryGIlXI8hITqJB116ENGxcoEfi3PKvWbtsCwBOWgmdRkSnFdFpHdC5aXFz0KF1dMDByYXsPzbRuEkYD4wcjIOrJ1o3L9TOHvxw4hJGx7xei5wMGjSI1q1bExISwuXLl/nwww/p0KEDR44coWbNmvnuc+HCBb744gvGjRuHi4sLKoutku21pEwCAvz56LXhOMeaUEXEk3gVQkURjYcTDqFV8KgSgENMGs4Xoxj7/U+4u+bWBElPT6fph035dOaX9mNt2rSJSZMm4ebqSgudFQcXRVpdwYZifCgoKCgUgFqjpX7nbpzavgWVWoPG0ZGzu3fQ5YmxNO8/ON99qrbsBcu20KVLfZqPn1lo/8IrX+DXuCO1B+SOr7ip31EYc+bMsX/u2LEjvXr1Ijw8nE8++YRvvvkmT3u9Xs+DDz5IjRo1+PDDDwEwZcQDoHXy5sTJDdQICCRoeB+aHo7FzdWLV199NVcfERER/Lx8Rx7DQ5ZlRo8ezfjx4wkODgZssSuZmZns37+fZb8vYfKE5xjnrOh8KNhQjA8FBQWFAhAEgT7PTaLLqGcQRRG1TseyGe+y7ecFaB2daNitV559Mq9H2/5Pvl5k/56enqSlpeVZn5KSYg9ALS4BAQF06NCBQ4cO5dlmMpkYMmQIKSkp7NmzB2dnZ9v6LFv13iT9NVIux6Mb1o1HBr/Anm0TqRte/MyU119/HU9Pz1yZN4GBgTz44IMAtG/ZgusZmTgqng+FGyg6HwoKCgpF4ODsgtbRCVFUMeCl10GW2ThvDoteeY6dv/5I5ImjLJ85jejTJ0k4uQeAVmPfLrLf8PBwzp7NXYslLS2NuLg4wsPDy2TskiQxcuRIDh06xLp16+yeCQDvoLboLWrUh44Rl5HBoIEvkJGRwbp16+jdu3ex+p87dy5Hjhzh22+/zbV+0KBBbNu2DYC9e/bi6eRYLkG7CvcmivGhoKCgUAIcXFwIDKuHg6sb7lUCOLDqL/784C0uH97PXzPe4Vq6bcrk6LT+cO1UoX317duXTZs2kZqaal+3dOlSRFGkV6+8XpXCiI2NZdeuXbRs2TLX+gkTJrBq1SpWrFiRJxi1Wp1+HAsfiWx14M1xw+nVszdNmjRh8uTJeHt7069fP2JjYwEYPXo0bdu25fjx4wQFBbF06VIAnn/+eSIiImjZsiVNmjSx64eMHTuWo0eP0qBBA96ePp2hLRoqMR8KdgS5stSNvoFer8fd3Z20tDTc3BQpXgUFhcpN7PkzRJ85hVdgMCtmvQ8IiFjpX/UsdR59HVo/W+C+KSkp1K9fnzp16jB16lS7yNjIkSP56quv7O26d+9OZGQkFy9eBOC3335j9erV9OvXj6pVq3L58mVmzJhBcnIyhw4dokaNGgBMnz6dN998k1dffdU+BXKTevXq4ebmhmyx8PMzA3HUiQz9dk3ZXyDg2D/r2LTgGyb9trJS1MtRKB9K8vxWYj4UFBQU7oCqdepStY5Nv+LZb3/EycMDS8R+Mr4bAhnXQJahgAeup6cnmzdv5oUXXmDw4MG4uroyduxYe0DoTaxWKxaLxb5co0YNYmNjmThxIqmpqXh4eNCtWzemTZtmNzwANm7cCMCsWbOYNWtWrj63bt1Kly5dENRqmnfvwvqVO7mwbA6hD5a9xLohIx2tk7NieCjYUTwfCgoKCmWNKZOsadXIsmjw7vMyQpf85dErC6a0RBY+/xhqQeKpnzaWef/bF//A+b27ePqrH8q8b4XKQ0me30rMh4KCgkJZo3XG6dVjeLYaTPKWuRC5p6JHVChadx+quIm4uzkCBRe7KwmzZ89GEAQGDBiAPj0ZrZNzeQxd4R5FmXZRUFBQKA/cA1H1n4Xz5X/Rf/cgbh/EVfSICkTOSiE+xULDVvULLHaXlZWVKw4lJ1bJSmT8RS5En+TS9fOcjjjNd2/+jtZVw66rO4lXR9E4uBqj7vJ5KVReFONDQUFBobzQuaBrOpTUjXNx/PUJNP2ng3tQRY8qD0mH1pNl1eLfqH3Bxe6ee47+w3qQJl0nIukSUelRxJuukyinkqYxYFXdmsGPWxyDT0NPrClWXFTOaCwiHmE1Cjq8wn2IYnwoKCgolCNCt7fw96lD2ur3sHzSAa+eLyC0ex7UuooeGgCy1crWJb/gpjNhqhHC9xNfplbDED7Z8BaxmXFcsyaRYEjFKll5+qfn8ezoicoq4G5ywEdwp4E2lGDXIKp516KOf12SojJ4+NQjnDh3iuHDh+Pi4oKrqxpfj/KpDKxwb6IYHwoKCgrliSBA40dwD+uLtPUjktd/hm7/ElwGz4DaPSp2bLLMp1+9wPLGyWSpMpizYxSREVF4dvRkc8YePK0uVFF50di7Lpc8I2hnasFHPT6imn9tVKq8jw+r1Urz4c158803CQi4ZWzoTXrcde5388wUKjlKwKmCgoLC3cDBDbHvdLwnbkLt7kfSgscxLR4BqVfvqNvSBofKksSm79/hnNHM5W1XODr2BMK3EmTB+JbPsu/pI7zf/DPi/shi0Ssr0KdksHbZJj58exbXriXk2+c333xDZmYmL7/8cq71BosBJ7XTHZ2nQtHIkkzkqWjO7bmIxWKt6OEUimJ8KCgoKNxNqtTFYewqvEd8ieHqcZI+6YS841OwlCybBLAHh5pMJpYtW8b06dOZP38+kyZNKnQ/yWphzTdT2RUjMtAlgOSVyajd1GRbTQiCgJODi73/8PBwvvjiC6pVq0bz5s3ZvHkzffr0wWg05uozISGBt99+m88++wytVptrm06lI9uSXeLzUyiarPRsTu04z4lt5zi18zxu3i5UbxTC4fUnMWQZi+6gglB0PhQUFBQqCmM60pYZJO/4ESffQJyGfAI1OhV79xkzZvDhhx9y9epVe3Do/Pnzee6557h69SpVq1bNs4/VmM3fX/+Pk3onBjYJYPayPcjI/L3/bwSdgEOcA0899RQzZszItV9gYCCPP/443bt3p1evXvz777+0a9fOvn3cuHGcOXOGFStW2NcNGDAAFxcXqo6uQrI5kd9HLEenqhyxLvc6hkwj5/ZcwsFVR63m1VGrc1dCtloljv5zmuZ9Gty1MSk6HwoKCgr3AjpXxL7T8XlpAzh6kjj/EaxLx0L6tWLtvm7dOnr06JGrAu6wYcOQJMmubpoTc2Yqv3/xJqf0jjzcrjZZPg35+++/afZgMyQkquqqFlnsztvbGyDP1M7Zs2fZsWMHnp6e9p9///2XDRs2sHD4z1zae5F5x+aV9Aop3IYsy5z59yKXjkTSsGs4Ya1r5TE8AFQqkVrNgjm391IFjLJolIBTBQUFhYrGvwFOz67D8eivpKz+APXptrj1eR1aPAX5BHbe5OzZs4wZMybXOg8PDwICAvIYEMbUBH77dgbRRmdGdG9KjXYP0Ly5LTh0fep6HHCglm8tOnTowPTp0+2y7QC///47oihSs2ZNXnvtNZo1a0aHDh1y9T979uxcBfIAJk6ciKOjIzNmzGAf+/hr78/0r9mfWh61Sn+t7jNkWeZ6VBIJEckACMhUbxKC8w1BuMLwqOJOckwqCZGJVKnmU95DLRGK8aGgoKBQGRBFhGaP4RXeD+O6d0n8+z3cDv2G9qFvwK9evrukpKTYDYSceHp6kpycbF/Oun6VxfM/J8nsyBMDOhPSoidffvmlPTh0/5L9GDFitBgZN24cX375JYMHD7YXu3vuueewWq107tyZFi1asHbtWnr37p2r2F2TJk3yjMPDwwMXFxe6dOlCW2tb9sfu5INt77DggZ8QBcXxXhAp8WnEXrjGzaAIr6ru1O8YWqraODWbVuPIP6fwCvBAra08j3zl21dQUFCoTDh5oXtoDj7PrSA720jKnJ6wfRZYzaXqLj3mHAvnzibVomP00P6EtOiZJzh0SucpyMhcSrxkL3anVqsZPHgwb7zxBqNGjWLnzp0sXrwYo9FIjx49MJlMuYrdFYVOpeONbu9wIfo0f134q1Tn8l9Fn5jBqZ3nObnjHCd3nCM9SU+99qE06FSHBp3qULW23x0V5WvYNZwT286V4YjvnMpjBikoKCgo3CK4Je4vbcOyZQZJG2bjdHwljg9/DQGN7E08PT1JS0vLs2tKSgpeXl6kXDrMT4t/wYqaMY89ik+tJgC8/fbbNGrUiI4dO5Kamkpt99p4iV4kmhL5deevDGs7jE2bNuXpt0OHDvTo0YNq1arxwQcf8MorrxR6Ctu2bcu13NK/Jc3qtGP+9i/oU70PrlrXkl+X/wDpKVlEnYpGkmyuDWd3B8Lb1UalKh9/gFqtIqR+ABcPXaF288qhNKsYHwoKCgqVFbUOda938W7wABm/P0/Sl/3w6voUQqdXQedSaHBooKvID4t/Qy2IjBkzFo+gOvY2OYNDb2dkp5G4r3anf//++Q7Jz8+PoKAg+3RLcbmedZ3P933K4fO7qeEfel9Nu2SkZnH1ZDSS1WZsOLrpqNMm/0DR8sI70Ivk2DSSY1Pxqupx145bEIrxoaCgoFDZqdoUlxe24rzrc5I3fYPmwF+4DXiHvn36MH3GjFzBoUuXLkUUBa4lp1LV05nHn3kR1yrBuborKDjUIBsw9jVyTV1wtk1UVBSRkZHUrFmzWEM3S2Z+Pf0rP++eh0qlZmKvqQypPQSVePcevHcbu7Fxw7Ph6HL3jY38CG1Zg8PrT+JexRVVBY9F0flQUFBQuJdIicS0Zirpp7Zg8q1P84+PUye8nj04dOKLL1CvbhhPDu7GY+NfxcmzCt27d88VHJofXbp0wcXFheyHsxFNIv888w/jxo3Dx8eHFi1a4O7uzrlz5/j000/Jzs7m2LFj9rTbgtgft59Ptk8nPimKXk0e4IUWL/0nZdYz07KIPJHb2KjWMBi1pvIZWCnX9KQnphFSP7joxiWkJM9vxfOhoKCgcC/hWQ3tY7/gfWkLGSum8tcgmbf2X2bw4ME46TQ0atiAJ/q35/EX38LBxfagt1qtxQ4Oba9tz3JhOZ+s+YRWrVoxf/58vv76a4xGIyEhIfTr14+pU6cWanjEZ8bz2Z5Z7D67lVpVw5n36GLqetctk9OvDGTps4g8EYPVKgHg6KyjTutaldLYuB1RpbIbSRWJ4vlQUFBQuFexmpH3zSN14+fEyL78KfShhouR4c+/jdahaB2I/DAYDTyy8BEiHCJYM2gNQV5Bxd7XbDXz46kf+W3PArQaB8Z3fpkBNQfc8/EdWfpsIk9EYb0Rs+HgpKV645B7wti4nfTkTJJiEqnesFqZ9614PhQUFBTuB1QahHbP49lwKBd//xjxUjbJTl5cjogkLCysVOmZDjoHPhn8CQ9ueJDNZzYzqv2oYu23L24fH2/7gMTkOPo1f5jnmk24Z7NZ8hgbzjpCW9ZCrb33jI18kSp6AIrxoaCgoHDv4+pHy7GfEhQXx/o1q/njx4UE1qhFn/79CQwMLHF3Jtkmne6idimy7fWs63yy52P+PbOZOkH1mT78E8K8wkp8zIokK91A5PEo+zSKg5P2v2Vs5EAQBKRKMOGhGB8KCgoK/xECAgIY/dRYLl68yIZVK1nw1RwatGhFz169cHUtvhciIjECgBCvkALbJGUnsfzicpbsWYhareHVPu8wqNagOxLDulvYjI2rWK0yAqB11FC7RXU0Ok1FD63cqSzfj2J8KCgoKPyHEASB0NBQak18mUOHDrFl7RrOHj9Gp569aNOmDWp10bf96NRoAGr45i9IlZCVQPel3XHNVOPrE8hPD/xSqbNYDBlGrhy/itUiIwigdVBTu0WN+8LYuB2dsxZ9QmZFD0MxPhQUFBT+i4iiSMuWLWnQoAFbNm1iy9rVHNq7hz4DB1GnTp1C34BTMlMQZAEvt1vVchOzE1l3ZR0dAjvwycFPAEh3tiBejyEmI6bSGR8p8WnEnIsHQOuopnaz6mgc7j9j43bUGhUah4p/9Ff8CBQUFBQUyg1HR0f6DxxIy9atWbtyBb8v+oFqdcLoN2Agvr6++e5jkS2IsogoilxKvcS/Mf+yP34/26O38/GBjwF4ssGTLDy5EINW4uDl3dTzzr/43d3k2pXrXL9qK6jn5uNE/U6FG1kKFYdifCgoKCjcB1SpUoVRT43l3LlzrF/5N3Nnf0aLDp3o0qULjo6503I9tB5YRSsnE04yfN1w+/oXmr7A0YSj7IzZycOhD/Nys5fJNGdiTEhGluW7/qCXZZnoM7GkXc8AwDvIgwad761g14pAslb0CO7Q+Pjoo4+YMmUKL730ErNnzwZg/vz5/Prrrxw+fJj09PQCSz4rKCgoKNxdBEEgPDyc2rVfYffu3ez6ZyMnDh6ge7/+eHh4cOHCBTw8PLh67Cp4Yzc8Hgp9iJebv4y7zp3E7ESupF0hxM0Wc79BJwAAHTZJREFUjOqidcGpqiPJsdF4B5a9aubtWEwmrhy/ijHT9gQNqF2F4Holz+i5X0m9loqzZ+k0YMqSUhsfBw4cYN68eTRq1CjX+qysLPr06UOfPn2YMmXKHQ9QQUFBQaFsUavVdOrUiSZNmrBx3TrW/PkHskqNrLbFRLgF3hKIGtd4HBOaTLAv+zj64OPok6s/UVShdXTEmJWJzsm5zMebEpfAtStpWMwSokpFtYbBOLvryvw4JSEiIoLz58/Tq1evCh1HSbgelUTs+QQadQuv6KGUzvjIyMhg5MiRfPfdd3zwwQe5tk2cOBHIW0pZQUFBQaFy4ebmxsOPPEJUu3asXr6M63Gx1KrXgIBOgfy+7Xcc1Y6Mbzy+WH25evmQFH21TIwPY1Y2V0/FYMq2eTdcvD2o3aJmpdLdiIiIYOPGjfeE8SHLMuf3X0GlEWncvXLI3JfK+JgwYQL9+/enR48eeYyPkmI0GjEajfZlvV5/R/0pKCgoKJSM4OBgxr/4EgCp1+I5FLMftUXg0fqPlkgaXVSVzjiQJImYM9Hok7IBUOu0VKtfDQeXislOkWWZ559/nuPHj6NWq3FycmLmzJk0aNCAV155hQEDBvDtt9+ye/duDh48yLJly/Dy8iq647uMLMmcP3AZQ6aJag2r4uFbeTKSSmx8LFmyhMOHD3PgwIEyGcCMGTN47733yqQvBQUFBYU7w8PPn+5+g1jpE0ZAUK0S7StJxdftjklI4cileOR4EzW9HQgIrUpw/YJFze4mq1atQhRFdu7cCcATTzyRp8348eMJDg7mk08+udvDKxRZkrl8NJLsDCOyJFOzaTWc3Ss+xuN2SmR8REVF8dJLL/HPP//g4OBQJgOYMmUKkyZNsi/r9XqCg8s/aElBQUFBIX9M2Vl4uvqiFkv2furm7UtSTFS+gacGk5k9JyNJN5gB8PN0oW/rcNb9c4X6nWqWybjLijNnztC5c2f7siqHR6eS1WK1I1klzu+/jMlgoUbjIFy9ipbGr0hK9Jt16NAhEhISaNasmX2d1Wplx44dfPXVVxiNxlxfUnHQ6XTodBUbOKSgoKCgcIvM1BQ8A0qeQaJxcLAbIF5VgzgdEc+luBRAQKMWaFe/Bu7Ot93vK6EMR926ddm0aRMPP/wwAJ6enkRHR9OgQQOOHz/OwIED0Wg0WK0Vn7NqtUqc23sJq8lK7RbVcHStfF6O/ChRnePu3btz4sQJjh49av9p0aIFI0eO5OjRoyU2PBQUFBQU/ltoHByIMTuwbNdp1CoVg9rVY1C7uvRtFY67sw6j0cK6DZcxmiwVPdQCGThwIBaLhQ4dOtC1a1ceeOABXn/9dR566CGcnJwAaNiwIYcOHWLo0KGkpqbe9TFaLVZO7TjPqe3nqNkkhIZdw+8ZwwNK6PlwdXWlQYMGudY5Ozvj7e1tXx8fH098fDwXL14E4MSJE7i6uhISElIpA3IUFBQUFIpPYSmmsiyz+UwC1bydeKhj/Xz337IrmoYNffln21UsZon46PTyHnKJEQSBb775Jte6Y8eO5Wm3Y8eOuzUkOyajmXN7LiFLMmFta6Fz1N71MZQFZa5wOnfu3FwBpJ06dQJg4cKFjB49uqwPp6CgoKBQhmSkJOPgWnBMX0EppqlZJrafv0638Cq4FlJDRa0SMGZZGNDLFueRojcW2FbhFtnp2Vw4EIGoEqnbtjZq3b0tUC7IlSx6Rq/X4+7uTlpaGm5ubkXvoKCgoKBQJsiyzNgnR3Pi9B60WmccHXW8PKk9Pbq/ydSp03OlmNaqVcueYno6Vk9CuoHOdXyLJbG+bOV5HhxU5y6c0b2P/rqeKydi0DlqCW1VA5WqRNESd5WSPL/vbdNJQUFBQaHMWLVqFZKUyq5de9BqvXniiScICnyMlJQ9ZGVFIkmWXCmmtmmWawR7OdElrEqxj6PWqJAkGVGshNGmlYTrUUnEXbyOk7sDjbqG/+cK5CnGh4KCgoICAMd2b6ZTqArTpbVo6z6OSqVCFDX4+fVHp1tPevppwIrBGE9KZjY7LiQXOc2SH54uWhLSDPhXghojlY3YC/Fcj0rB09+NRl0rXga9vKi8/hsFBQUFhbtK7areHL7qgkUfAdxKMQU4efIs7u6N8PFphUp0Z+3hjQxqXLXEhgdAq9YBbN0cUYYjv/eJPBHF8a1nEVUijbvVJeQ/XixPMT4UFBQUFLCYzQxsEUJ26mW6j/+Lxo0bF5Jieoy5731OWlpaqY6l06pxdtZitVS8TkZFE3EimmObz+Dq7UKjruH41yz+9NW9jBJwqqCgoKBAcuR5PMxXSU/az5HsdqxevbpA6fAlu9YxoHknXBxLX0Tu0tU0UpIMtGjqV+o+7mUiT0aTlpBBYFgVvAP/GzIUSsCpgoKCgkK+5Fc07aOPPqJa+lFe+W0HPRu7suifgoumbTmxm/bhje7I8ACoHujK6TOJwP1lfFw9HUNKvJ6qoVWo1iCooodTYSjGh4KCgsJ9RH5F0zLP7MClVVPQHMRqzSy0aNqltEu0rtO8VMfOKVBWmVNGy5rs9GwuHY5ElgX8a/n85+M5ioNifCgoKCjcR9xeNE0UBMTMeMTq7YA/QZa5HLEGWZa5ln6Z/VfXIIoiwv/bu/Owpu50D+DfkwAJgUS2gGxFaRWx2Gp1VFoLtSoW16rt9LbWWlu10z5t587UpfQ61S46D7czncV2fDrt1W6jtc7cVu14adWCuCsgooIgW0UWiWwJi4Ek7/3DEo1sAcM5Ib6f5+GPJCfh/XIgefmd3/kdCLCQBaH+nvjbid1Y+eBjvf7eXS1Q5myICOcOVkChckNghBqDtKrev4aFUHSqFC36VihUboh+YBjkbnwJknbcfDDG2G3k5oumeRsvo8ZzEoBrl8MYP34W6hqLcEGXg8Ml/8K8USs6rDFhPpaG336wAe+//Ib1vs4O5yQnJyMmJgYrVqywWaCs/XCOs9FfacGVS42Qu8lw9kA5Hn5mBJrqjb1qPmrKa1FRoAMEYMi9oVD7OvfVZaVy+4x7McYYs7lo2kPx8ZgVNxav//4DLFiwAAZjJZpaarDsl++ivtSCbe+c6PSMFvVVoCrnAj44+m/rJeZvPJyTmpoKf3//Ds978cUX8cQTTyAtLc0pr/VVXlCHyNFaRMT444k1v4BfsH3zWkxGE3IPFeBMaj6aGloQ89BwjHooihuPbvDIB2OM3UZuvGha/flj8PG04JGX1uPqVT0OZ3yAhx9IwssvvwwiwpUrV7Bw4cIuRzCKcs+jenU1kqYv6nA458arnHd1UqWH0g0GgxFqtaJ/Q/eAiFBV1ACF6vqaJfasKFqeX4naigbI5DIMGz8EHsqBeZE3KfDIB2OM3aZUJ/8C3BELANh78gM8MP7XvRrBOH7oKF55+FFsy0lFdHS0zVVeb1ygLCcnBwDg7u4Os/n62h53DvXBheL6fkzYsyuXGlGUpYNvsBciR2t73L6+qh5n0s7jTOp5uCvdMWryCNwdN5wbj17i5oMxxm5HRJDpy4Cf/8Ovt3gj+dz/YX/mYbtHMCwWC77NPYF7BkfaHM6ZPHlyNwuUZeLxxx9HfX09IkK8UXW5SYSwnasqboAgAHeNDYTSq+uVWht0epxJy8eZtHw06AyIiY/CqMkjEBgRIGK1roUPuzDG2G3IWJELmvgK3ACYzSbEeIVj0Zh5ePbgKaSnp2PcuHEoKCiwjmDExMQgJycHs2fPto5g/PehrzErKhYxQREAYD2c0+706dMdvu+NoyMAYJZwncvACDXK8urgH9pxbkZbqxH1l3WouKCHf5g3YuKHu9zF3aTEzQdjjN1miAjGi2eh/sU85OamoeVKESLumQkAeGLB0/jL62swe/ZsNDQ04IsvvsCrr76Kjz/+2GYEIykpCY05J/Grfz0iZZRbIpPLQBbb5qepvg7G5ia4uXtAGx4GbbhExbk4bj4YY+w209DQgJXvfYycyveBtiYEBEUgOXkCAnwGY/+f/gdDHh6HxnoDSk+UY+3atUhNTe1wdkp6ejo2Hf83fHx8pAnhIJoAT9RfbgJZ6kEEePn4wMvHV+qyXB43H4wxdpvZ+/33cFOp8eafXsKM8Y9i8bPP2jz+aPg4+Dyhwh1hYXjqv37d9WmxA/wwRFluOeqrG6H2c0dEzBAIMp4GKRZuPhhj7DbRvrz52ZzTmPLoQjzfEomv65s6TCpVurnjkbB7sUv1EzJ1+ZgVdm+nr2e42ipW6Q5DRCjMKEVLoxHBd2kxipc6lwS3eYwx5mJamxrRUFwEi9EIAGipqEDVt98g8/PP8M2mvyEqIACfpezFa0OCEOen7vK02DaTCYM8vNFiaoHJYrL5HtUGA+qMV265VrEmcZpM5p8XAjuPwXcG4J7JI6AN73gaMRMHj3wwxpgLaF/e/HR2NsyXq6D29cfqaVNx94TxeGfbV3jshRfw9blcHMnIxJmSn+ARfTd2Lnoce9zdsW7dui4nlWatykPlb8/h0ehJiA25tiaIyWzByr0b8dZDS6WMbJerTUZcOFkCQQCGjY+EwpPX43AG3HwwxpgLaF8c7NsPNsJLrcYLb7+DoKeewuCYGHgePARBEKxXq3054RG03P8Aor09rc/v7rTYf5WXQO1xfT2O3x/chuSEX2Gwt/Mtkd6uprwW5QXVcPdww8hJfFE3Z8PNB2OMuYD25c3N1dVQDI/qdnEws9mM+jbTzS/RJR+lP2pbyqy3o/wj8ElGCt6I+w/InGSSJlkIpWfL0FTfAhAwKFCNUQ9F8docTso5fmsYY4zdkvblzd0CtWgpLu52efPWu+6CT9lFu1632tiGvKp0mC3Xl0WPi7wLWi8NDv90wfFBeoGIUJJzEWfS8pF7+AL8Q3wQExeFmPgohEeHcOPhxHjkgzHGXMDs2bORkpKCxKeehtfgwZ3O4xg5NBLHU1JQfPw4fvXKfyL6nru7fD0iQoa+GTm6Mwh2N6Le2IaMqgy0mFpgNLXhQqUFw9U+SC/QYdJdAZDJxPugr62oQ8WFagBA2MjBGHrPHaJ9b+YY3HwwxpgLaL9abdU3/4vB8+YDsJ3HQW1t0Kd8j8O5uaC2Nhw6fc7m+QaTGecaW2Boa8Xlpouo0BdjmJcayrZqRPqPRKAqEL4KX6jcrzUy8eEWuMtlOFZcg1azBUpZ/86paL3aisKMUphNhEGB3rg7jpc7H8i4+WCMMRegv1SGxiNHoAwO7vAYmUyo//Zb+MydC0EQIHh44GxQKNpq9NZtvOUyKNsuodZQjEbDRUzwi4aFLLjXPxaRPpEdXtNd7qij9rbzUS6X6qC7WGttLJRe7rja2AqCgBETIuGm4I8tV8B7kTHGXIBh3z6EPLPYZpVOIkLt5s2ATA7fhU9B8Lh+mumS0AB43tRAHCmvhZ+nHx4Z+ghkgkhTAgmov6xHWV4FAAH+YdfmbbTX32wwwsNDDndl11edZQMPNx+MMeYC5L4+No1HW3k5Gg8fBrWZ4P/cIpvGA0CHxgMA7g+9v8vXb6qvQ2tL8/U7fh6ZGCwzYX+m7vrdEGABQQYBBALhWhMhAAj188aIocEgsiA9uxhGkwUyuQX1ugbExEVBuGneiCAI8NIoe/FTYAMFNx+MMTbANdfXwVJ27cwWY1ERjEVF8AgPh+8vf9n1c/QNMDY1AoIAshAEAfAJCu5wfZPG2hoYm5vh7efX6QXXfAEMGdJzjRYL4ZKuAYdzimEhIH70nfBU8GjG7Uqgm08Al5her8egQYPQ0NAAjUYjdTmMMeb0dMeOQFGvh6BUwk2rhUfEEMi9vayPm9raYKjRAe1v94IAT28NlN7e1m3IYkFdVSWILPAJCoZMLseVi6VQ+2tttmOsK735/OaRD8YYG+BUI6JhajRAE3YHiAiNtTUwGeqtj8vd3K+NanRzdoggk8EvJBREhIbLVbja1IjAoZGQ9fNZLOz2xM0HY4wNcF4+vjB6KFBbUQ5BALz9/OGuCOjTawmCAJ/BHc+YYcyRuPlgjDEXoFCpoPh5MTHGnB0vr84YY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRuUldwM2ICACg1+slroQxxhhj9mr/3G7/HO+O0zUfBoMBABAeHi5xJYwxxhjrLYPBgEGDBnW7jUD2tCgislgsqKiogFqthiAInW6j1+sRHh6OsrIyaDQakSsUh6tndPV8gOtndPV8gOtndPV8gOtndKZ8RASDwYCQkBDIZN3P6nC6kQ+ZTIawsDC7ttVoNJL/sPubq2d09XyA62d09XyA62d09XyA62d0lnw9jXi04wmnjDHGGBMVNx+MMcYYE9WAbD4UCgXWrl0LhUIhdSn9xtUzuno+wPUzuno+wPUzuno+wPUzDtR8TjfhlDHGGGOubUCOfDDGGGNs4OLmgzHGGGOi4uaDMcYYY6Li5oMxxhhjohpwzUdBQQHmzp2LgIAAaDQaTJo0CampqTbbXLx4ETNnzoRKpUJgYCBWrlwJk8kkUcW9k5aWBkEQOv06efKkdbuvv/4ao0ePhkqlQkREBN577z0Jq7afvfm+//57TJw4EWq1GlqtFgsWLEBpaal0hfeCPRnXrVvX6eNeXl4SV98ze/chEeEPf/gDhg8fDoVCgdDQUKxfv17Cyu1nT8bS0tJOHz927JjE1ffM3n3YrrCwEGq1Gj4+PuIX20f2ZMzPz8fkyZMRFBQEpVKJyMhIrFmzBm1tbRJX3zN78qWlpWHu3LkIDg6Gl5cXRo8ejX/84x8SV/4zGmCGDRtGM2bMoNOnT1NBQQG99NJLpFKpqLKykoiITCYTxcTE0NSpU+nUqVO0Z88eCggIoKSkJIkrt4/RaKTKykqbr6VLl9LQoUPJYrEQEdGePXvIzc2NNm3aREVFRfTdd99RcHAwbdy4UeLqe2ZPvuLiYlIoFJSUlESFhYWUmZlJcXFxNGbMGImrt489GQ0GQ4dtRo4cSYsXL5a2eDvYk4+I6JVXXqGoqCjauXMnFRcXU0ZGBv3www8SVm4/ezKWlJQQANq3b5/Ndq2trRJX3zN79yERUWtrK40bN44SExNp0KBB0hTcB/ZkLCoqos2bN1N2djaVlpbSzp07KTAwcEB8XtiTb/369bRmzRo6fPgwFRYW0p///GeSyWS0e/duiasnGlDNh06nIwCUnp5uvU+v1xMA2rt3LxFd+2CWyWRUVVVl3WbTpk2k0WjIaDSKXvOtam1tJa1WS2+//bb1vieffJIee+wxm+3++te/UlhYWIc3DmfXWb4dO3aQm5sbmc1m6327du0iQRAGxBv7zTrLeLPs7OwOv9sDRWf5cnNzyc3Njc6fPy9hZY7TWcb25uPUqVPSFeYg3f2Orlq1ip5++mnasmXLgGo+bmbP3yER0W9+8xuaNGmSSFU5jr35ZsyYQUuWLBGpqq4NqMMu/v7+iIqKwueff46mpiaYTCZ89NFHCAwMxNixYwEAR48exahRoxAUFGR93vTp06HX63Hu3DmpSu+zXbt2oaamBkuWLLHeZzQaoVQqbbbz9PTEpUuX8NNPP4ld4i3pLN/YsWMhk8mwZcsWmM1mNDQ04IsvvsDUqVPh7u4uYbV901nGm33yyScYPnw4HnzwQRErc4zO8u3evRuRkZH47rvvMHToUAwZMgRLly5FbW2thJX2XXf7cM6cOQgMDMSkSZOwa9cuCaq7dV3l+/HHH7Fjxw58+OGHElXmOPb8HRYWFiIlJQXx8fEiVuYY9uQDgIaGBvj5+YlUVTek7n56q6ysjMaOHUuCIJBcLqfg4GDKysqyPr5s2TJKSEiweU5TUxMBoD179ohd7i1LTEykxMREm/s++ugjUqlUtG/fPjKbzZSfn08jRowgAHTkyBGJKu2bzvIREaWlpVFgYCDJ5XICQLGxsVRXVyd+gQ7QVcZ2LS0t5OvrS8nJySJW5Tid5XvhhRdIoVDQhAkTKD09nVJTU2n06NE0efJkiaq8NZ1l1Ol09Mc//pGOHTtGJ06coNWrV5MgCLRz506Jquy7zvJduXKFwsPD6cCBA0REA37ko7u/w9jYWFIoFASAli9fbjPqOlD09D5DRLR9+3by8PCgs2fPilRV15yi+Vi9ejUB6PYrLy+PLBYLzZkzhxITE+nQoUOUmZlJL774IoWGhlJFRQUROW/zYW/GG5WVlZFMJqN//vOfNvdbLBZatWoVKZVKksvl5OvrS+vWrSMAdOzYMTFjWTkyX2VlJQ0bNoxWrlxJWVlZdODAAYqPj6cpU6ZIeljJkRlvtHXrVnJzc7M5VCgFR+ZbtmwZAaD8/HzrfZmZmQRA0kMx/bUP2y1atEjSIXtH5ps3bx6tXr3aettZmo/+2IcXL16kc+fO0datWyk0NFTSfwT663f0xx9/JJVKRZ999ll/R7CLUyyvrtPpUFNT0+02kZGROHjwIBISElBXV2dz6eBhw4bh+eefx+uvv44333wTu3btQnZ2tvXxkpISREZGIisrC2PGjOmvGN2yN6OHh4f19jvvvIONGzeivLy808MNZrMZVVVV0Gq12L9/P2bMmIHq6mpotVqH198TR+b73e9+h5SUFJtZ95cuXUJ4eDiOHj2KiRMnOj6AHfpjHwLAlClToNFo8M033zi03t5yZL61a9diw4YNNmcNtLS0QKVS4YcffsC0adMcH8AO/bUP23344Yd49913UVlZ6ZB6e8uR+Xx8fNDY2Gi9TUSwWCyQy+X4+9//jueee87xAezQ3/vwyy+/xPLly2EwGCCXyx1Sc2/0R74DBw5g5syZeP/997F8+XKH19wXblIXAABardauD8zm5mYAgExmO1VFJpPBYrEAAGJjY7F+/XpUV1cjMDAQALB3715oNBqMHDnSwZXbz96M7YgIW7ZswTPPPNPlH4tcLkdoaCgAYNu2bYiNjZWk8QAcm6+5ubnDPm5/E2jfz1Loj31YUlKC1NRUp5gr4Mh8DzzwAEwmE4qKinDnnXcCuHaaPABEREQ4ruhe6o99eKPs7GwEBwffSom3xJH5jh49CrPZbL29c+dOJCcn48iRI9b3HSn09z60WCxoa2uzNlpic3S+tLQ0zJo1C8nJyU7TeAAYWHM+dDod+fv70/z58yk7O5vy8/NpxYoV5O7uTtnZ2UR0/VTbhIQEys7OppSUFNJqtQPi1Kkb7du3r9PhNaJrP4dNmzZRXl4enTp1il599VVSKpV0/PhxCSrtm+7y7d+/nwRBoLfeeosKCgooMzOTpk+fThEREdTc3CxBtX3TXcZ2a9asoZCQEDKZTCJW5hjd5TObzXTfffdRXFwcZWVlUUZGBk2YMIGmTZsmQaV9113GTz/9lLZu3Up5eXmUl5dH69evJ5lMRps3b5ag0r6x53e0nbMcdumt7jJ++eWXtH37dsrNzaWioiLavn07hYSE0MKFCyWotG+6y9d+qCUpKcnmlNyamhoJKrU1oJoPIqKTJ09SQkIC+fn5kVqtpokTJ3aYy1FaWkqJiYnk6elJAQEB9Nprr1FbW5tEFffNk08+Sffff3+nj+l0Opo4cSJ5eXmRSqWiKVOmSDbXo6+6y0dEtG3bNhozZgx5eXmRVqulOXPm2PUG6Ux6ymg2myksLIzeeOMNEatynJ7ylZeX0/z588nb25uCgoLo2WefdYo3vd7oLuOnn35K0dHRpFKpSKPR0Pjx42nHjh0iV3hretqHNxqozUd3Gb/66iu67777yNvbm7y8vGjkyJG0YcMGamlpEbnKvusu3+LFizudMxIfHy9ukZ1wijkfjDHGGLt9DKh1PhhjjDE28HHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVFx88EYY4wxUXHzwRhjjDFRcfPBGGOMMVH9Pzqqr4CilOfMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Removed all whole districts.  Finalize stats before squishing in outcroppings.\")\n",
    "trueCountyPop = [countyPop[c] for c in range(nCounties)]\n",
    "trueCountyGeom = countyGeom.copy()\n",
    "countyPop = [0. for c in range(nCounties)]\n",
    "trueStatePop, true_nDistricts = np.sum(trueCountyPop), nDistricts\n",
    "trueCountyTractList = [list() for c in range(nCounties)]\n",
    "# minTractPop = 4.5  #already defined above\n",
    "populatedTractList = list()\n",
    "countyTractList = [list() for c in range(nCounties)]\n",
    "statePop, excludedPop = 0., 0.\n",
    "\n",
    "for t in range(nTracts):\n",
    "    trueCountyTractList[countyNo[t]].append(t)\n",
    "    if t in allCutVTDs or t in skipList:  #  or vtdPop[t] < minTractPop:  #need to keep low-pop vtds as VEST may have assigned votes to them\n",
    "        excludedPop += tractPop[t]\n",
    "    else:\n",
    "        populatedTractList.append(t)\n",
    "        countyTractList[countyNo[t]].append(t)\n",
    "        countyPop[countyNo[t]] += tractPop[t]\n",
    "        statePop += tractPop[t]\n",
    "        #tractPop[t] = max(tractPop[t],0.000001)  #avoid possible later div/zero errors for nil-vote vtds\n",
    "print(\"Of the total statePop\",statePop+excludedPop,\"we ignore\",excludedPop,\"as it is cut out of the map\") #,\n",
    "#\"or in sparsely populated areas (<\",minTractPop,\") per geom\")\n",
    "print(\"This excluded pop comes from a total of\",nTracts -len(populatedTractList),\"tracts\")\n",
    "true_nDistricts = nDistricts\n",
    "nDistricts -= nCutDistricts\n",
    "print(\"Our final adjusted number of state districts, excluded fixed cut-out is\",nDistricts,\"rather than original\",true_nDistricts)\n",
    "aDP = statePop/float(nDistricts)\n",
    "print(\"Each district will be drawn to house\",r3(aDP),\"= 1/\",nDistricts,\"of non-cutout state pop\",statePop,\"vs original=\",trueStatePop)\n",
    "print(\"Here is a county-level modified map with each county's fraction of a district pop\")\n",
    "\n",
    "vtdGeom = tractGeom.copy()\n",
    "nVTDs = len(vtdGeom)\n",
    "\n",
    "cutCountyList, uncutCountyList = list(), list()\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"working on county\",c)\n",
    "    if len(countyTractList[c]) == 0:  #no vtds added to county b/c all were in cut lists:\n",
    "        cutCountyList.append(c)\n",
    "    else:\n",
    "        uncutCountyList.append(c)\n",
    "        countyGeom[c] = tractGeom[countyTractList[c][0]]\n",
    "        for t in countyTractList[c]:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "uncutMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "print(\"here is the\",STATE,\"map excluding cut and unpop coastal tracts\")\n",
    "\n",
    "for c in uncutCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "    FONTSIZE = 11\n",
    "    if countyPop[c] / statePop < 0.02:\n",
    "        FONTSIZE = 7\n",
    "        plotCenter(r3(countyPop[c]/aDP),countyGeom[c], FONTSIZE)\n",
    "    else:\n",
    "        plotCenter(round(countyPop[c]/aDP,2),countyGeom[c], FONTSIZE)\n",
    "plotPoly(trueMAP,0.1)\n",
    "for c in cutCountyList:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "    plotCenter(\"cut\",countyGeom[c],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0062ac21-3328-4616-99ed-70c53e62b308",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the map topology before any squish operations.  Plot countyPop/aDP\n",
      "Working on county  0 distances\n",
      "Working on county  20 distances\n",
      "Working on county  40 distances\n",
      "Working on county  60 distances\n",
      "the max LAT-scaled distance between counties is 4.90523\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter user-picked max district diameter, or 0 to accept 4.90523  3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on county  0 neighbors\n",
      "Working on county  20 neighbors\n",
      "Working on county  60 neighbors\n",
      "I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For corners and outcroppings, we fuse county clusters\n",
    "print(\"Now finalize the map topology before any squish operations.  Plot countyPop/aDP\")\n",
    "preSquishCountyGeom = [countyGeom[c] for c in range(nCounties)]\n",
    "maxD = 0.\n",
    "for c in range(nCounties):\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"distances\")\n",
    "    if c not in cutCountyList:\n",
    "        plotPoly(countyGeom[c],0.2)\n",
    "        plotCenter(r3(countyPop[c]/aDP), countyGeom[c], 7 )\n",
    "        for cc in range(c+1, nCounties):\n",
    "            dist = getLongDist(countyCP[c],countyCP[cc],xScale)\n",
    "            if dist > maxD and cc not in cutCountyList:\n",
    "                maxD = dist\n",
    "                #print(c,cc,maxD)\n",
    "print(\"the max LAT-scaled distance between counties is\",r5(maxD))\n",
    "plotPoly(MAP)\n",
    "plotPoly(MAP.centroid.buffer(0.5*maxD))\n",
    "plt.show()\n",
    "inputD = float( input(\"enter user-picked max district diameter, or 0 to accept \"+str(r5(maxD))+\" \") )\n",
    "if inputD > 0:\n",
    "    maxD = inputD\n",
    "neighborCountyList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"Working on county \",c,\"neighbors\")\n",
    "    for cc in uncutCountyList:\n",
    "        if cc > c and cc not in cutCountyList:  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"I have also identified each county's neighbors.  Let's visualize the counties with two or fewer neighbors\")\n",
    "hasFewNeighborCs, has1neighborCs = list(), list()\n",
    "plotPoly(MAP,0.15)\n",
    "for c in uncutCountyList:\n",
    "    if len(neighborCountyList[c]) == 2:\n",
    "        hasFewNeighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.2,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.5)\n",
    "    if len(neighborCountyList[c]) < 2:\n",
    "        has1neighborCs.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c+0.1,countyGeom[c])\n",
    "        for cc in neighborCountyList[c] :\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9b3df93-d821-44ca-b4f4-b65d13baaffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing from above, develop corner county blocks from each low-pop corner county until it hits 0.25 of 777517\n",
      "checking if county 6 with pop 127657.0 and 1 or 2 neighbors is less pop than 194379\n",
      "checking if county 9 with pop 79843.0 and 1 or 2 neighbors is less pop than 194379\n",
      "checking if county 59 with pop 307855.0 and 1 or 2 neighbors is less pop than 194379\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below I plot these again by county no, with faded neighboring counties\n",
      "0   [6]\n",
      "1   [9, 15, 16, 20]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are 777517.0 194379\n",
      "You may suggest [ [6], [9,15,16]  ] \n",
      "let's decide whether to delete, accept, or modify list [6] with pop 127657.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's decide whether to delete, accept, or modify list [9, 15, 16, 20] with pop 169886.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list) [9,15,16]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, I will replace [9, 15, 16, 20] with [9, 15, 16]\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if you want to manually add another corner-county cluster 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our final pops and fused-county lists are...\n",
      "127657.0 [6]\n",
      "164779.0 [9, 15, 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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qEwfMaXpY8pKULmXM0Oblwd/cDBEIKF3KkGgnTAUA+Gs/VbiSwbU0ysi19QC6+PiMMdQQEY0xclDGsX0tsOQnIdUau/PwRII2Lw8IBBBoaVG6lCHRFF0GSSXgP1mrdCkDCYFeZxeSJ0wG4mScI4YaIqIxpNftx7GqFhRPscCcqle6nDFHm5sLAPCfPj3qbXV3d6O6uhrV1dUAPpsk8tSp8PXZkTQaaJNV8DeMwc7NkoSssmw07fsYCMZGy9elMNQQEY0RHXY37McTY8qDkQqFmjBMl/Dhhx9i+vTpmD59OoC+SSKnT5+Ohx9+eNTbPpc2wwh/iyOs2wwX0+euR2+PDDRVK11KWMT+9VtERHGg8Vgn1BoViidnKl3KmKYymaDOyIAvDC01EZ0k8hxaazo6PziN3ndeheGLN0R8f8MjQUAFCFnpQsKCLTVERAo7+YkD5lQ9bMW8wmkotLm5MTOxJQDoy/uugDp9//9P4UoGMXEpTGage/smwN+rdDWjxlBDRKQQIQSOV7fCkpuEVKtR6XJihjYvLyx9aqIl/Ue/gHX5DPhcMoR/jM3crVIj5+s/QtsJB5ybvwl0je3Rjy+Fp5+IiBQghEBtVSvyJqbBmKRTupyYos3LQ++h2JmnSFKpYJgxB3i9Cv4jVdBdPkeROhraDqGh5SDaXSfh6DoNh6cF7b0dcPhccBS1oadnPIpf+lds+rf/i9lZ30fVUrNx40ZIkoTVq1cPeE4IgaVLlw5p5tPbbrsNkiT1uy1ZsmQ0pRERjVkBfxBHK5tReHkGA80IaHNz4W9qgpBjpx+IrryvM7L/8H5F9m9veB9L/++ruKPycdxf8xKeatiB7W0fodbdCJUcQHlSAfRWF4770vDKkVcUqTEcRtxSU1lZic2bN2PKlCmDPr9p06ZhJb0lS5Zgy5Ytocd6PS9lJKL409PlQ0NNB8bPzIJKzR4AI6HNywX8fgRaW6G12ZQuZ0i0E6YBkoDv+BGYFdh/j9x32ut6YcbaZVtgSisBtIYB6z3413V4aecfcN2462DQDHx+rBvRv6ju7m6sWLECzz//PNLTB85FUl1djZ/97Gd44YUXhrxNvV6P7Ozs0G2w7RIRxTK/N4jTRzpRNsvGQDMK2rw8AOEZqyZaJGMStEkS/GEcA2eourrsWLXzbgDAVz73fZislw0aaADgliu+AlWvFq8f2xbNEsNmRP+q7r77bixbtgwLFy4c8JzH48HXv/51PPPMM8jOzh7yNnft2oWsrCxMnDgRd911FxyOC1/T7/V64XK5+t2IiMYyIQTqDrRi3HSr0qXEPG3u2VATO1dAAYA2wwBfU3NU9uXubsb/7X4UXncL7nplCdolgYeyv4Spl3/toq+blDEJk6YW4fc7/wKP3xOVWsNp2Keftm7diqqqKlRWVg76/Pe//31ceeWVWL58+ZC3uWTJEtx4440oKSlBbW0tHnjgASxduhR79uyBWj1wAKoNGzZg3bp1wy2diEgxJw46UFSRCUkVmx0wxxJ1khnq1NSYaqkBAM/JXgCRvWy6/tQ/8Kt/rsPr3iYISQLq/giogZ9ffhcWzrr7kq+XJAl3fe5O/NvB+7C3aS+uLrw6ovWG27BCTX19Pe69917s2LEDBsPApqtt27bh7bffxv79w+sIdfPNN4fuT548GVOmTMG4ceOwa9cuLFiwYMD6a9euxZo1a0KPXS4XCgoKhrVPIqJosdc5kW4zQW/kBafhosmLrbFqAMBQnIneE20R2353tx0P7vwujsKP25LGwWrOhV5jgEZnGlKgOaswuRCphhTsb94f36Fm3759aGlpwYwZM0LLgsEgdu/ejaeffhp33XUXamtrkZaW1u91N910E77whS9g165dQ9pPaWkpMjMzcezYsUFDjV6vZ0diIooZvV1+ZJekKl1GXNHF2Fg1AJBxzZVo/OU2yM52qFIzRrUtIQQ+PPBrHLVXotPrxOttVWhUS9BKAr+ouAufH0aIOZ8kSci0pqGxuXVUNSphWKFmwYIFOHjwYL9lK1euRHl5Oe6//35kZmbizjvv7Pf85MmT8fOf/xzXXnvtkPfT0NAAh8OBnJyc4ZRHRDTm9HT7YEjWKl1G3NHm5qH7nXeULmNYtCV9Iwv7a/ZDf8XAP9hDZBme7iY4Oo/D0XkCjq4GOLrtEDojPld2Pextn+JnB57FEdF3KksjBL4kGXFrzjxcOemrKM6fO+paTUl6uDrivE9NcnIyKioq+i0zm82wWCyh5YN1Di4sLERJSUnocXl5OTZs2IAbbrgB3d3dWLduHW666SZkZ2ejtrYW9913H8aPH4/FixeP5D0REY0ZrSe7kF/OqznDTZt3ZqwaIWJmoDhV6SQAQO3eV/C2cxsOOo9hqjoZjt52OHxdcAR70IYAHJJAj+oC1/Gc+j8AQFkQ2Dzuq6iY+i0ERBAZ6aVhrbXIlo8dR98L6zajQZETvDU1NXA6nQAAtVqNAwcO4H/+53/Q2dmJ3NxcLFq0CI8//jhPMRFRTPP2BBDwybx8OwK0eXkQXi+CbW3QWJW/okwIge72ZtiPf4z2U0fQXV8Hb+NpwN4KTZsT5nYPMjqDAIBfnHoX/0jp+0wclQUs0MKiNqDQlIXp+nRYjBZkmrNhScqDJbUIlvRSqFUaHKl5HYCAWp+GyeU3QqtPitj7cXQ7kKQ3RWz7kTLqUHOpfjKDzYB67jKj0Yg333xztGUQEY0p7k4vmutcKJnGWbcj4exYNb6GhqiGmmMfvYPTH+5GT/0pBO12qFs6YHB0I6XDC+OZaZ1SAJhUgDNFDbfFBK81Bb6KcejOyUVSZirumVGBh60TYM2YAI1m6CNKf+6K70bmTZ3H5XOhqroGX7hixqVXHmPYFZ+IKMw67G50OXpRyjFpIkabnw8A8DecBqZPj9p+nd/6DrK8QJdRgitDD68lCV2Ti9GTkw1jXgFSC8cjq/gyZBVMhFYbm2cb/nTkVQSDMr5RsULpUoaNoYaIKIwaj3ZCrVGh8HKL0qXENXVSEtTp6fA31Ed1v+4kLexfHI9lT/0pqvuNFiEE/nmwEqWlubCaYi+U80QvEVEYCFng+P5WJKXrYStJUbqchKDNz4evoSGq++zOSYH6dEtU9xlN//H+z9Da6MLyadcoXcqIsKWGiGiU7HVOdLd7UVRhgVY/cBR0igxdQT789dENNXJ+NpIrj0R1n9FS31WPd/fsx/WLFmB+wXylyxkRttQQEY2QkAVOHGiDzqDB+JlZDDRRps0vgC/Kp590JcXIaPfD1xt7Y7hcSpevC7IqgGlZU5UuZcTYUkNENAS+3gAaj3ZCkiQIIRD0y1BpVMifmM4woxBtfh4C9mYInw+SbuhXEY1G2oTLoRL/h6bDVSiaNi8q+4yWsrQyJJlM+MuhNzDTNlPpckaEoYaI6CI8Lh/sx53QGdQonJTBMWfGEF1BASDL8Dc1QVdUFJV95k+aDSeAppr4CzVatRazp03Gvv2HgflKVzMyDDVERBfQctIFX08ApdNi7yqQRKA9M5Gxr74haqEmu6AcTXrAdeTTqOwv2jLNmfD4Y/fUGv/kICI6jywLHK9uhc6gQX756CYepMjRZmcDajX8UbwCSqVSoSPLCP+Jk1HbZzSZtCZ4gz7IQla6lBFhSw0REYBetx/2405A9IWawkkZ0OjYV2YskzQaaHNyoj5WTW+eBfrTsTeD9VBkGjMhCQknXCdQmhre+aSigS01RJTQZFng5McOtDV0o6jCguIpmSidZmWgiRF9Y9Wcjuo+VUWFSLfH7imai5mbOxcGjQF/PvpnpUsZEYYaIkpoHU1upGebkD8xPWZme6bP9I1VE92WGnPJOJh7BZyt0R0jJxr0aj1Ky3JwqKZO6VJGhKGGiBKa3xuE3sQz8bFKm5cf1T41AJA+fhIAoLGmKqr7jQYhBJpPdyIrN03pUkaEoYaIEpq/N8hxZmKYNjcHQacTsid6p4Nyy/vGcGk/+knU9hkN/qAfGz7YgA6nE9dd9i9KlzMiDDVElLCEEPC4vBx7JoZpsrMBAH67PWr7tFjy4TRL8NTVRm2fkRaUg7jzT/dg7z8PYfrll8Xs4Hv8l0xECSnol3H0w2YUVWQqXQqNgjY3FwDgb2qK2j4lSUKn1YBgfXg7KO/evRvXXnstcnNzIUkSXnvttbBu/2JeOLAFHad6cdu1N+GxRQ/HbP8yhhoiSiiuth6cONCG+sPtGDcjC4YkrdIl0Shos7IASUIgiqEGAHqz06FrcoR1m263G1OnTsUzzzwT1u1eyvHO49i2+y1M/1wZbii7Pqr7Djf2jiOiuNTe5EaXoxeQgLN/c8qyQHKGAcVT2DoTLySdDprMTPibonf6CQBEXjZSDoR3n0uXLsXSpUvDus1L2d2wG7/48wswmLT4t899J6r7jgSGGiKKOwF/EK62HhRPZnhJBJrcnKiefgIAc+l4mD1V6GlvgzEjNj9nHr8HP331OWTkGfGzpT+DxWhRuqRR4+knIoo7DYc7UHAZpzdIBL01RwB/AP6mxqjuN3N8BQCg4XBlVPcbTu+efheSX4VVc26Pi0ADMNQQUZzx+4JQqSSoNfz1lggaf/AD9H76KTSZ0Z10tOCyKwAALUc+iup+w+mjlo9gNBgwN2eu0qWEDf/VE1FcaTjcgfzydKXLoChRp6YiZdky5P3Hk1Hdb6a1EC6ThO7jR6O633A6VtuAvEJLzF7pNBj2qSGiuOHrDUCjU3HcmQSiyc6G3x7Z/jRyMIhO+0m0njqMzvpadDfWw9fchAwhEKiPzakSPm77GM32diyZdaPSpYQVQw0RxYUOuxuO026Mmx7d0xCkLG22DT3V1SN+vdzTg0BzMzrqj6Ou9kN4mhrgt9sh2hzQOrpg7OhBclcAGrlv/RQAZglwJavgsppgmjsnLO8DALq7u3Hs2LHQ47q6OlRXVyMjIwOFhYVh2w8AbN69Bfp0CdeWXhvW7SqNoYaIYlpvtx+nj3QgzWbC+JlZSpdDUaaxZSPQ3Awhy5BUn7XQCVlG0OGAv7kFgZZmBJqb4W9uRqC5pe9+SzMCLa2QXa7Qa1IBaPSAK1WDnjQTvDmpcE8phdOWDXNuAdIKSpGZPwHWvPHQavVhfy8ffvghrr766tDjNWvWAABuvfVWvPjii2HdV2qaGb3d6dCq42ucJoYaIopJwaCM+k/aodGrUTrdGlf9AmjoNNk2CL8f9nWPIdjRcSawtCDQ2goEAp+tqFZDY7VCY8uCNssG85y5ffdtNmiybPjv5tewq/cAXvl/26BWKTMX2Pz58yGEiMq+uro80Ojj7zQtQw0RxZzOZg/am9wovDwDGi0no0xkhssmQZObA8++D6HNskFXWgrT3Dl9YeVMYNFkWaGxWCCpL/xZsRw6hlP7diREOK531eNEnR1f+fISpUsJO4YaIoo57Y1ulLLvDAHQ5eeh7O23R72dguQCeINetHhakG3ODkNlY9eL1S9Br9fiKxNvUrqUsIu/ticiimv2407YSlKULoPiTFFKEQDgpOukwpVE3rGa05gxfSKMGqPSpYQdQw0RxQwhBDxOH8xp4e+kSYkt15wLtaTGqa5TSpcSUR6/Bw6PA2UZZUqXEhGjCjUbN26EJElYvXr1gOeEEFi6dOmQpk8XQuDhhx9GTk4OjEYjFi5ciKNHY3dAIyKKjMYjncidkKZ0GRSHtGotcsw5qHfVK11KRNV01EAIIM2QpnQpETHiUFNZWYnNmzdjypQpgz6/adOmIXe4evLJJ/HUU0/hueeew969e2E2m7F48WL09vaOtDwiijNyUIavNwCDOb4uQaWxozClMO5PP5WmliIlzYSnX9+Cvx7frnQ5YTeiUNPd3Y0VK1bg+eefR3r6wOHIq6ur8bOf/QwvvPDCJbclhMCmTZvw4IMPYvny5ZgyZQpeeuklNDY2XrKFh4gSR8PhDuRzkkqKoMLkwrg//ZSqT8V/rXga4ybm4dm//A+6fF1KlxRWIwo1d999N5YtW4aFCxcOeM7j8eDrX/86nnnmGWRnX7oHeV1dHex2e79tpaamYvbs2dizZ89IyiOiOBPwByEEoNXx8m2KnMKUQtR31UMWstKlRJRZa8ZtM76JgBxEnbNO6XLCatiXdG/duhVVVVWorBx8uvXvf//7uPLKK7F8+fIhbc9utwMAbDZbv+U2my303Pm8Xi+8Xm/oseucESGJKP7Uf9qOwgqL0mVQnCtMLkyYy7qb3c0QkkBuUq7SpYTVsEJNfX097r33XuzYsQMGg2HA89u2bcPbb7+N/fv3h63AwWzYsAHr1q2L6D6IaGzobPYgKd0ANSeppAgrTOmbX6m+qz7uQ02dqw4mtQkWQ3z9sTCs3xL79u1DS0sLZsyYAY1GA41Gg3feeQdPPfUUNBoNduzYgdraWqSlpYWeB4CbbroJ8+fPH3SbZ09RNTc391ve3Nx8wdNXa9euhdPpDN3q6+O7tzpRopJlgbaGblgLk5UuhRLA2XFbTrniu18NAHh8HkiQEBRBpUsJq2G11CxYsAAHDx7st2zlypUoLy/H/fffj8zMTNx55539np88eTJ+/vOf49prB58JtKSkBNnZ2di5cyemTZsGoO900t69e3HXXXcN+hq9Xg+9nuNUEMW7kwfbUDw5vv6SpLFp58mdeOz9x5BhyMD0rOlKlxNxV+ZdiTcD/0RtZy0mZkxUupywGVaoSU5ORkVFRb9lZrMZFosltHyw1pXCwkKUlJSEHpeXl2PDhg244YYbQuPcrF+/HmVlZSgpKcFDDz2E3NxcXH/99SN4S0QUD86edtKwczBFUJevCxs/2IhttdtwdcHVeGTuI7AY4z9Ip+hTICDDG/ReeuUYosjcTzU1NXA6naHH9913H9xuN1atWoXOzk7MmzcP27dvH7TfDhHFv7OnncbPzFK6FIpjHzR9gAf/+SBcPhce//zjWD5ueUJMaAkAbT1tACQk6+Lr1O6oQ82uXbsu+vxg06ifv0ySJDz22GN47LHHRlsOEcUBnnaiSNt5cidW71qNWbZZeHHei3F3FdClaCQNJEjoDcTXILe8nICIxpTOZg+SMnjaiSKrMKUQJo0JJq0JWabEaxGcbpsOk86EN479VelSwoqhhojGDDkow9HYDWtBfDWJ09hTll6Gn8//Od47/R7Wv79+0LMK8Uyr0mL61In4YN/HcPQ4lC4nbBhqiGjMOPlJO4o4yB5FyZV5V2Ld59fhj0f/iOcOPKd0OVH37en/ioDaj4fffDxuQh1DDRGNCR12N5LS9dBoedqJoue6cdfhe9O/h19W/xJ/OvonpcuJqixTFr57ze04VdeMqpYqpcsJC0WufiIiOlcwKKO90Y1xMxKvbwMp79uTv41mTzMe2/MYMo2ZuCr/KqVLipoccw4EBNx+t9KlhAVbaohIcScPOFA8JVPpMihBSZKEtVesxVX5V+EH7/wAH7d9rHRJUfN+0/sAJFyWcZnSpYQFQw0RKcpxuhtp2SaoNfx1RMpRq9T4yVU/QVl6Ge7eeTfqXYkx/U5xSjF0QQPeqHtD6VLCgr9FiEgxQb8MZ2sPMnLMSpdCBKPGiKe/9DRSdCn4zlvfQXtvOxq6GvDn2j/jvw7+F771129hxRsrUNNeo3SpYeENevHU9l/BYAOWlixVupywYJ8aIlLMyY8dKJrCq51o7Eg3pOPZhc/iG298A1f//mrIQgYAmDQmTLVOxV77Xnzlz1/Bpqs34eqCq+Hxe5CkS1K46pFp7G6Erwv48ufmxM1YPZKIg+u4XC4XUlNT4XQ6kZKSonQ5RDQEjsZuQACWvNj8QqD49qnjU9z11l2YXzAfa2auQYouBZIkYXfDbmz5eAs+bP4QGkmDgAigwlKB68dfj6WlS5GiGxvfQbKQ0d7bjmZPM5rdzWjxtITut/a0oq2nDS2eFvR6/LB1FeMv926FSor+yZtwf38z1BBR1AWDMk4caMO46fHx1yHFJ1nIF/yi39e8D+/Uv4PT3afRG+zF7obdGJ82Hq8ufzUqtXmDXtjddjS5m9DU3dT3/3Pu2912+GRfaH2NSoMsYxayTFmwmqywGq2wmqywGCzIbC/C9AmTkJQe/fkWw/39zdNPRBR1Jw86UFzBq51obLtYy8VM20zMtM0MPV6zaw0OtB4Iy35lIcPR44DdbYfdYw+FF7vbHgotjt7+owBbjVbkmHOQbc7GZZbLkG3ORo45BzazDTaTDRmGjIu+n7oDbdDo1DCYtWF5D0phqCGiqOqwu5GSaYRay+sUKH4c6TiCiRkTL7meEAJOrzMUVs7ezoaWZk8zmj3NCMiB0Gv0aj2yzdnINmejLL0MV+VfhZykHOSYc5BrzoXNbINOrRtV/UUVFpz6xIHiybH9xwZDDRFFjRyU0d7k5mkniiveoBcnXSfx7cnfhtvv7hdWzg8vzZ5m9AR6Qq/VSBpkmbKQbc6GzWzD1KypyDZlh1pdss3ZSNOnQZKkiL4HR0M30rNj/ypEhhoiipqTHzs4txPFnU8dnwIAHvrnQ3jonw+FlkuQkGnMDIWT8WnjQ/fPnh6yGCxQq5SdGkSWBbocvSidblW0jnBgqCGiqOhs9iAp3cC5nSjulKWV4faK25GkS+oLLKa+0GIz2aBVj/0+Kp12DzIL4+MqRIYaIoo4WRZwnO7m3E4Ul5J0SVg9c7XSZYxYV0cvCsrTlS4jLNhTj4gi7tTHDhTytBPRmOP3BRH0y1Cp4yMOxMe7IKIxy9nqgSlVB62Op52IxpoTH7XF1WSyDDVEFDFCFmir70ZWEQfFJBprTn3iQMFlGVCpIntlVTQx1BBRxDSfcCFvQnycqyeKN7IsYEga+x2Zh4Ohhogiptftj7tfmkTxQA7KER/7RgkMNUQUEXJQjqtmbaJ4cvpIJ3LGpypdRtgx1BBRRNiPu2Arjb9fmkSxLuALIuCXoTPE36gu8feOKOpcbT1oa+iGSi1BGsFf5hd7hSz3TSLPv/iHJxgUUKkHHrNoHsWAX4beyF8xRGPRub8fgv6+6UsAwNsTgM8TgCFJi9yyNIWqGzn+xqEROxtmki0GlE6L/eG1iYgSgUanhhyQQ48bj3YixWqE3xtAVlEytDo16j5qU7DCkWOooWERQqDlZBc8Ti+S0hlmiIhi0bmdhN0uLwomZfR7PsVqxOkjHTF39SJDDQ1JwBfE6SOdkIMyrIUpsBVz3BEioljkcvTAnKYHAHhcvkHXycxPQt2B2GutYaihi3K19cDR6IZGo0J+eTrUGvYtJyKKZZIkocvRC0ueGVq9OhRwziUHZZw40IaccakwmGNnWAaGGhpAyAJNx53wuv1IthhREkdDaBMRJbrkDAOMSVqc/NgBrycw6ESzKrUKX/hqGWr3t0KlllA2y6ZApcPHUEMh3p4Amo52QgAxl86JiGjoNDo1SqZevE+kRqfGxNnZcDR2o/7T9gH9bsaiUZ1L2LhxIyRJwurVq0PL7rzzTowbNw5GoxFWqxXLly/H4cOHL7qd2267DZIk9bstWbJkNKXRMLQ3uVF3oA1t9V0orLCgZEomAw0REQEALLlJcDl6lC5jSEbcUlNZWYnNmzdjypQp/ZbPnDkTK1asQGFhIdrb2/Hoo49i0aJFqKurg1p94Vl6lyxZgi1btoQe6/UDz/FR+MhBGY1HO+H3yUi3mXiKiYiILkhn1EDIYkRjkUXTiEJNd3c3VqxYgeeffx7r16/v99yqVatC94uLi7F+/XpMnToVJ06cwLhx4y64Tb1ej+zs7JGUQ8PgcfnQXOeEpJKQW5YWlyNKEhFReKk1qjEfaIARnn66++67sWzZMixcuPCi67ndbmzZsgUlJSUoKCi46Lq7du1CVlYWJk6ciLvuugsOh+OC63q9Xrhcrn43urC+sWVcqDvQBpejB8VTMlE8OZOBhoiIhkSjjY0rX4f9rbZ161ZUVVWhsrLyguv88pe/xH333Qe3242JEydix44d0Ol0F1x/yZIluPHGG1FSUoLa2lo88MADWLp0Kfbs2TPoKasNGzZg3bp1wy094QQDMk7XdCAYFLAWJCGriGPLEBFR/JKEEGKoK9fX12PWrFnYsWNHqC/N/PnzMW3aNGzatCm0ntPpREtLC5qamvDTn/4Up0+fxj//+U8YDIYh7ef48eMYN24c3nrrLSxYsGDA816vF16vN/TY5XKhoKAATqcTKSn84u7p8sF+3AmVWoW8iWnQaC/cl4mIiOhSTn3iQOHllrBv1+VyITU1NWzf38Nqqdm3bx9aWlowY8aM0LJgMIjdu3fj6aefhtfrhVqtRmpqKlJTU1FWVoY5c+YgPT0dr776Km655ZYh7ae0tBSZmZk4duzYoKFGr9ezI/Eg2hvdcLb1wJikRfGUzH7DYBMREY3UkFs/FDasULNgwQIcPHiw37KVK1eivLwc999//6CnioQQEEL0a1m5lIaGBjgcDuTk5AynvIQkZIHGY53w9QaRns2rmIiIKHENK9QkJyejoqKi3zKz2QyLxYKKigocP34cL7/8MhYtWgSr1YqGhgZs3LgRRqMR11xzTeg15eXl2LBhA2644QZ0d3dj3bp1uOmmm5CdnY3a2lrcd999GD9+PBYvXhyedxmnutp70XSsE0UVFuhNHFeGiIgiI1ba/cPandlgMODdd9/FNddcg/Hjx+NrX/sakpOT8d577yEr67NhmGtqauB0OgEAarUaBw4cwHXXXYcJEybg9ttvx8yZM/Huu+/yFNMlJKXrodGqGWiIiIgwzI7CY1W4OxrFktb6LugMaqRaTUqXQkREcSpWOgrHxoXndEHWgmS0NXQrXQYREcWxWGn9YKiJA7biVNjrnEqXQUREpCiGmjiQlK6Hx+mDHJSVLoWIiOJQQnYUJuUUTMpA/aEOpcsgIiJSDENNnNDq1FBpJHg9fqVLISKiOCIH5ZiYzBJgqIkr+RPTcbqmU+kyiIgojgT8csxMaBkbVdKQSJKEFKsBnS0epUshIqI4EfDJ0OhiYw5Bhpo4k5mfDMdpXuJNREThEfAFodHFRlyIjSppWLJLUmE/zku8iYho9AJ+ttSQgsxpenhcvMSbiIhGL+ALsk8NKatwUgZOfdqudBlERBTjAn4ZaoYaUpJGp4ZGp0avm5d4ExHRyAXZUZjGgrwJaTh9hAPyERHRyAkhoOI4NaQ0SZKQmZ+MEwfalC6FiIhiVKxMZgkw1MS9VKsRap0KwQA7DRMRUXxjqEkApmQderp8SpdBREQxKDZOPPVhqEkAxmQderrYYZiIiOIbQ00C6Gx2IyXToHQZREREEcVQkwBspaloquUIw0RENHzsKExjilqtgiTF0llRIiKi4WOoSQDBgAyVmqGGiIiGL5a+PRhqEoBao0LQz0u6iYgovjHUJAgpRkaDJCKisYV9amjM8XuDSpdAREQUUQw1CeDkJw5k5icpXQYREVFEMdTEOV9vAL6eANJsJqVLISKiGBRLnRcYauKczqCBNkamjCciIhoNhpo419Plg1bPUENERPGPoSbOnfq0HYYkLYQcS/3XiYiIhk+jdAEUWROusKGrvRf1h9ohzuSa1Cwj0rLYx4aIiOILQ02ckyQJKRYjUizG0LLaqhaGGiIiuiRZFjE1zc6oTj9t3LgRkiRh9erVoWV33nknxo0bB6PRCKvViuXLl+Pw4cMX3Y4QAg8//DBycnJgNBqxcOFCHD16dDSl0UWoNDzrSERElxbwBaHRxc53xogrraysxObNmzFlypR+y2fOnIktW7bg0KFDePPNNyGEwKJFixAMXnjwtyeffBJPPfUUnnvuOezduxdmsxmLFy9Gb2/vSMujC+hq70VSul7pMoiIKAb4vUFoYuhikxGFmu7ubqxYsQLPP/880tPT+z23atUqXHXVVSguLsaMGTOwfv161NfX48SJE4NuSwiBTZs24cEHH8Ty5csxZcoUvPTSS2hsbMRrr702kvLoIuy1Tg7ER0REQxLwBWNqWJARhZq7774by5Ytw8KFCy+6ntvtxpYtW1BSUoKCgoJB16mrq4Pdbu+3rdTUVMyePRt79uwZ9DVerxcul6vfjYZGZ9LE1PlRIiJSTsAnQxPPoWbr1q2oqqrChg0bLrjOL3/5SyQlJSEpKQl//etfsWPHDuh0ukHXtdvtAACbzdZvuc1mCz13vg0bNiA1NTV0u1BgooFEkJd2ExHR0Pi9QWj1cdqnpr6+Hvfeey9++9vfwmAwXHC9FStWYP/+/XjnnXcwYcIEfPWrXw1r/5i1a9fC6XSGbvX19WHbdrwzperQUNMBIRhuiIjo4vy+2OpTM6xLuvft24eWlhbMmDEjtCwYDGL37t14+umn4fV6oVarQy0oZWVlmDNnDtLT0/Hqq6/illtuGbDN7OxsAEBzczNycnJCy5ubmzFt2rRB69Dr9dDr2dl1JLKKUtDr9uN4dStSrUZk5icrXRIREY1RclBArY7TlpoFCxbg4MGDqK6uDt1mzZqFFStWoLq6Gmr1wDQnhIAQAl6vd9BtlpSUIDs7Gzt37gwtc7lc2Lt3L+bOnTvMt0NDYTBrMW56FgAJtftb0FzngsflU7osIiKiURlWS01ycjIqKir6LTObzbBYLKioqMDx48fx8ssvY9GiRbBarWhoaMDGjRthNBpxzTXXhF5TXl6ODRs24IYbbgiNc7N+/XqUlZWhpKQEDz30EHJzc3H99deH5U3S4DLzk2DJNcPbE0B7oxvuTi+shWy5ISKi2BTWEYUNBgPeffddbNq0CR0dHbDZbLjqqqvw3nvvISsrK7ReTU0NnE5n6PF9990Ht9uNVatWobOzE/PmzcP27dsv2m+HwkNSSTCYtcgtS8PJjx3w9QagM3CgaSIiij2SiIMeoy6XC6mpqXA6nUhJSVG6nJglB2U01HSgcJJF6VKIiGgMOPmJA0WXR+47Idzf37HT+4ciTqVWATEfcYmIKFxibVQzhhoK6evUrXQVREQ0VsTaVwJDDYW0nupCZgGnUCAiotjEUEMhHqcP5lSO/0NERLGJoYYAAD3dPhiStEqXQURENGIMNQQAaDzaCVsJrxwjIqLYxQFJEpyQBeo+akNmfjJn7yYiopjGUJPAfL0BnDjQhuIpmRxwj4iI+mk82oHk9NgaBJffZAnK2eqB47QbZZ+zsYWGiIgGCAYEUm1GpcsYFoaaBBMMymg43AG9UYPSaValyyEiojEq6Jdj7o9ehpoE4esNoPFIJyABeRPTodUNnFGdiIjoLEktQaViqKExxN3pRfMJF3QGNQorLDH3ASUiImWoVBKCQRlqdexcKM1QE6fam9xwtnhgStWjZGpmzDUhEhGRsix5SXA0dCOrKHaG+2CoiSNCCDTXueBx+ZCRY0bJVPaZISKikTGl6NB6qkvpMoaFoSYOyEEZp2s6EfAHYStJRXZpqtIlERFRHBCyQGt9F6wFyUqXMiQMNTGsX+ffCenQ6tn5l4iIwicj1wy/L6h0GUPGUBOD3E4vmutc0LLzLxERRVB7kxtFFRalyxgyhpoY0mF3o8PugSlVx86/REQUFbH0XcNQM4b5AjJkIaOz3g2Py4f0bBMHzCMioqjwevzQm7RKlzEsDDVjUKfHh9/uOYk3d52E7Jdx71cvx5dn5CldFhERJZDmOhfyy9OVLmNYGGrGkPp2D/5713Hs3XsaQQF87nM5aHJ7cecr1Vjb7cW3v1ASU82AREQUu2RZQBVDA+8BDDVjwkf1nfjvncdw6GArdAYNvvylYnzr88XITNIjKAv89G81eOKNQzhkd+HHN0yGQcurnIiIKHKCgdgaSfgshhqFyLLA32ta8NLfjuH0KRdS0vT45lcuw/+bWQDjOfMyqVUS7l9SjvLsZNz3hwM43urGr745E1kpsTUdPBERxY7mOidspbEzkvBZDDVR1usP4tWqBvzxrePobO9Fdl4SfrByOr48yQb1RS7NXj4tD8UWM1b9+kNc+/Q/8KtvzsLUgrToFU5ERAnD1xuEzhB7ESH2Ko5RTo8fv9lzAtv/fhI9PX5MnGTB2m9MxcyijCFvY2pBGv58zzys+vU+fHXzHjz5lSlYPo0diImIKHyEEEqXMGIMNRHW2NmDF945jn++1wBZBmZdkY1vXz0eJZnmEW0vK8WAravm4IE/HcS9W6tx2N6FHyyaeNFWHiIioqFqq++OmWkRzsdQEyFHmrvw/FvH8NH+Zmi0KiyYX4Rb55XAmqwf9bYNWjV+9tWpuCwnBRv+eggHGjqx6WvTw7JtIiJKbG6nF9ZChpqEJ4RA5YkOvPC3ozh2pB3mJB3+33VluGV2EZL04T3UkiThjqtKcXluCr63tRrXPPUu/v83T8OV4zLDuh8iIqJYIYlYPnl2hsvlQmpqKpxOJ1JSot9bW5YF/vZpM379t6NoPt2N9Ewjbl40HtdOzYVOE/lL4lq6evH9l6uxp9aBexdMwPyJVrR7fJg/wcpxbYiIaMhcjh74e4Ow5CVFZ39h/v5mqBkFbyCIP+1rwB92HIezoxd5hSm4dfF4XD0xK+phIigLPP32Mfz8rSOhZalGLf7+g/nIMOuiWgsREcWmEwfbUFRhidp3GEPNIKIdaly9fvxmz0n89e0T6PH0Xcl0+5fLMKNQ+eGkP6rvxAmHGw+++jG6vAEAQO2Pr2FHYiIiuqQTB9tQPDl63RjC/f09qnMjGzduhCRJWL16NQCgvb0d3/3udzFx4kQYjUYUFhbie9/7HpxO50W3c9ttt0GSpH63JUuWjKa0iLA7e7Hhz5/i5kf/jtffqMXkyZnY/KMv4JnbrxgTgQbou+x7+bQ8VD64ELozo0F+dfMe9PqDCldGRERjmbcnAJ0xtrvajrj6yspKbN68GVOmTAkta2xsRGNjI376059i0qRJOHnyJL7zne+gsbERf/jDHy66vSVLlmDLli2hx3r92LmS51hLF55/+xiq9zVDrVbhi18owMovlIzpUX0NWjWOPLEUfz3YhLt+W4UDDU5cUTL0MXGIiCixNNc5kTdxbPyBPlIjCjXd3d1YsWIFnn/+eaxfvz60vKKiAn/84x9Dj8eNG4cnnngC3/jGNxAIBKDRXHh3er0e2dnZIyknop59+xj+9JejMCZpceO/jMfXZxch2RA7U7GX2fouy2N/YSIiuhg5KGJyvqdzjaj6u+++G8uWLcPChQsvue7Z82QXCzQAsGvXLmRlZWHixIm466674HA4Lriu1+uFy+Xqd4uUwyc6Yc1LwisPX407vzg+pgINERHRUASDMlRx0Pdy2C01W7duRVVVFSorKy+5bltbGx5//HGsWrXqoustWbIEN954I0pKSlBbW4sHHngAS5cuxZ49e6BWD5yResOGDVi3bt1wSx+xJL0Geg1nxiYiovjUXOeCrST2JrA837BCTX19Pe69917s2LEDBsPF+5O4XC4sW7YMkyZNwqOPPnrRdW+++ebQ/cmTJ2PKlCkYN24cdu3ahQULFgxYf+3atVizZk2/fRUUFAznrRAREdEZvp4A9KbYPxMxrNNP+/btQ0tLC2bMmAGNRgONRoN33nkHTz31FDQaDYLBvitsurq6sGTJEiQnJ+PVV1+FVju8A1VaWorMzEwcO3Zs0Of1ej1SUlL63ejiYr9RkYiIIiEORnYJGVZLzYIFC3Dw4MF+y1auXIny8nLcf//9UKvVcLlcWLx4MfR6PbZt23bJFp3BNDQ0wOFwICcnZ9ivJSIioqFrb3IjI2dkkyyPNcNqqUlOTkZFRUW/m9lshsViQUVFBVwuFxYtWgS3243//u//hsvlgt1uh91uD7XiAEB5eTleffVVAH1XUv3whz/E+++/jxMnTmDnzp1Yvnw5xo8fj8WLF4f33Y5UTIfYmC6eiIgirMvRi5RMo9JlhEVYR9mpqqrC3r17AQDjx4/v91xdXR2Ki4sBADU1NaEB+dRqNQ4cOID/+Z//QWdnJ3Jzc7Fo0SI8/vjjY2OsGp63ISKiONbr9itdQtiMOtTs2rUrdH/+/PlDOjd37jpGoxFvvvnmaMsgIiKiYXI7vXFz6gkY5TQJNHrPPPMMiouLYTAYMHv2bHzwwQcR2Q8H3yMiovO1nuqCtSBZ6TLChqFGQS+//DLWrFmDRx55BFVVVZg6dSoWL16MlpYWpUsjIqIEIcXBoHtnMdQMRYT62v7nf/4n7rjjDqxcuRKTJk3Cc889B5PJhBdeeCEyOyQiIjoj4AtCo42vGBBf7yYCIpVffT4f9u3b12+qCZVKhYULF2LPnj1h209tqxsAUHWyM2zbJCKi2Gc/7kR2aarSZYQVQ41C2traEAwGYbPZ+i232Wyw2+1h209mkq5vu6ljd0ZxIiKKvoBfhkYXX1MAMdTEuTTTmVCTPAYujyciojFByPE5hhlDjUIyMzOhVqvR3Nzcb3lzczOys7PDtp+zp8/i8+NLREQj0Vrfhayi+JtiiKFGITqdDjNnzsTOnTtDy2RZxs6dOzF37tyw7Uc6cy13HE3tQUREo+Rx+WBK0SldRtiFdUTheCUi1M6xZs0a3HrrrZg1axauuOIKbNq0CW63GytXrgzbPj5rqWGqISKi+MZQcykRHLXua1/7GlpbW/Hwww/Dbrdj2rRp2L59+4DOw6Mh8fwTERGdw9XWg+SM+Lx4hKFGYffccw/uueeeyO8ofsZWIiKiUWhvcqOowqJ0GRHBPjVxjn1piIjofFKczp3DUJMgJDbVEBElPG9PADpj/J6kYagZglhu7Yjh0omIKMya65ywlcTfpdxnMdRcQry00MXL+yAiopGTgwJqdfx+9cfvOyMAgIjlZiYiIgobOShDFUczcg+GoSZBxPfHmIiILqX5RBeyiuP31BPAUBP32E5DREQA4PX4YTBrlS4johhqhiIOkkG8Xr5HRER0FkPNJcR6FmCXGiIi6rC7kWYzKV1GxDHUJIhYD2dERDRyztYepGUx1FDMY1MNERElBoaaBMGGGiKixNTr9kNviu8Owmcx1AyBiOHWDvapISJKbC0nXLAVJytdRlQw1MS5s5mGfWqIiBKTLAuo4ngU4XMlxrscBWYBIiKKVYkwivC5GGri3GennxLnQ01ERH0SYRThczHUEBERxalEGEX4XAw1ce5sJ2f2qSEiSjyJNpo8Q81Q8AoiIiKKQVqDGr6egNJlRA1DzaXEeMo926cmtt8FERGNRFZRMo5UNkPIifHX+ahCzcaNGyFJElavXg0AaG9vx3e/+11MnDgRRqMRhYWF+N73vgen03nR7Qgh8PDDDyMnJwdGoxELFy7E0aNHR1MaERFRwtNo1Ui2GCAHGWouqrKyEps3b8aUKVNCyxobG9HY2Iif/vSn+Pjjj/Hiiy9i+/btuP322y+6rSeffBJPPfUUnnvuOezduxdmsxmLFy9Gb2/vSMujM0ItNTHe4kRERCMjAVBrE+PEzIjeZXd3N1asWIHnn38e6enpoeUVFRX44x//iGuvvRbjxo3Dl770JTzxxBP485//jEBg8HN6Qghs2rQJDz74IJYvX44pU6bgpZdeQmNjI1577bURvSkiIiJKPJqRvOjuu+/GsmXLsHDhQqxfv/6i6zqdTqSkpECjGXxXdXV1sNvtWLhwYWhZamoqZs+ejT179uDmm28e8Bqv1wuv1xt67HK5RvI2hqzJ2Ysn/u/TS643lNaQobSXCPSFPQAQ8meTNAj5s6uZhHzO+kJASGfvn7OOADp6fJAEAFlAlsVn+5fC13ojhOjb75l9iGDf/y/ZwXqIuw+VeeZOv5dJ5697kY1e4ClJAiSVBEmS+u6P4riIvh/AZz/DMz8zIRA6HuJCc1eIQe/2m+ti0Jee/ZmLMz9/ce5nRHy27Nznz13vvOcBEfp8hX624rOf57mfSQkAzhy3swukcx6HjqV09sd35jkJkPr+06/b2gXXD+3ss3XO316/1599/pz9fLbsnM/Rua+TPlt27ms+Wy4N+AgN+HGc+7Ma8NyFHw+YiuXiD/u/9hJzoag1Kqg1ifFXOtGwQ83WrVtRVVWFysrKS67b1taGxx9/HKtWrbrgOna7HQBgs9n6LbfZbKHnzrdhwwasW7duGFWP3MwyCxrrnPh4X/OFVxrCqcqhnM0UEDj7a/PcX7h9C875ZX/Oc/1Xkc79PRx6yaLMVOgcPjS420PFDKWewb7axQWWS5IESS1BpZIgqc7+/8LbvuScVOeEswG/4EX/O+Kc1wxf3xe4EJ/9f9TO/WIPBSX0+/buH8ykwe4O2OZnd6UBy/uFsX7771v/7M8i9OWtkqA6++WukkLPhV5z3v1++xjkm70vC4nQz6Z/SDrnuJ4fvET/n3EoVInPQpl8fqg7J8Sd/5r+2xIXfM1ny898ys7dxrmrnfu86P9v9PyfwWCPB/w4z/sBXzygD3jxiPYT8Abh7Qlg3AwrT0NT3BtWqKmvr8e9996LHTt2wGAwXHRdl8uFZcuWYdKkSXj00UdHU+MAa9euxZo1a/rtq6CgIKz7OOvrc4rw9TlFEdk2EVE0NB7tgN8bhM4wosZ5opgxrDbJffv2oaWlBTNmzIBGo4FGo8E777yDp556ChqNBsFgEADQ1dWFJUuWIDk5Ga+++iq02guPZpidnQ0AaG7u3xLS3Nwceu58er0eKSkp/W5ERDQ4nVGLplpneFoiicawYYWaBQsW4ODBg6iurg7dZs2ahRUrVqC6uhpqtRoulwuLFi2CTqfDtm3bLtmiU1JSguzsbOzcuTO0zOVyYe/evZg7d+7I3hUREYVk5ichqygZ9Z+2o7PZg4AvqHRJFEWJFGWH1RaZnJyMioqKfsvMZjMsFgsqKipCgcbj8eA3v/kNXC5XqBOv1WqFWq0GAJSXl2PDhg244YYbQuPcrF+/HmVlZSgpKcFDDz2E3NxcXH/99eF5l0RECc6YpEOyxYAuRy9a67tQNst26RcRxZiwnmCtqqrC3r17AQDjx4/v91xdXR2Ki4sBADU1Nf0G5LvvvvvgdruxatUqdHZ2Yt68edi+ffslW3mIiGjo0rPNSM82w9XWg9ZTXbAWJitdEkVBInUPl0QcnGR1uVxITU0NXT5OREQXd+JAG4qnZCpdBkXBqU8cKLzconQZgwr39zcHLyAiSkSJ9Od7okugnzVDDRFRAkqUuYAS3bkDZyYChhoiogSkM6jR2exRugyKsIBPhkanVrqMqGGoISJKQLkT0uFo7A49loMyerp9CPiCaKp1XuSVFEu8Hj/0psQZdDFx3ikREfUTDMio3d8CtUYFryeAZIsBbfVdSLYYlS6NwqSn2w9Tik7pMqKGoYaIKAGpVBImfK5v1Ha/LwiNVgVJkpA7Pg2dzR4cr26FEALmND3SskwwmC88MjyNXb1uPzJyzEqXETUMNURECU57Xp+LNJsJaTYTAMDj8qHpWCdKplqVKI1GSQ6KhJqlPXHeKRERDZspRYfYH80scSXQ1dwA2FJDRESXYCtOQe3+FqjUKphTdcgq4iCnsSLR8ihbaoiI6KLMaXqMm56FjBwzet1+pcshuiCGGiIiuqTj1a3wewMoKM9QuhSiC+LpJyIiuihZFtBoVcjM5wSYsYZ9aoiIiM4hSX1j2pz82AG/N4ic8akwp+qVLotoAIYaIiK6KEmS+l3SXfdRKy/xpjGJfWqIiGhYVGp+dcQKXv1ERER0MYnWUSNGiQQcYIihhoiIhkyWBURQQMiJ94UZawI+GVpdYn3NJ9a7JSKiUTm2rxkqjcTWmhjQN0N3Ys3ZxVBDRERDNm5aFoIBAUliqhnrvJ4A9KbEuh6IoYaIiIZMrVXx1FOMYEsNERHRJfh7A0qXQEPg98nQsE8NERHRhenNifXXfyxLtNOEDDVERDQsepMWvh621tDYw1BDRETDotGp4PcGlS6DLiGx2mj6MNQQEdGwJKXp0dbQrXQZdAmJ2J07sa71IiKiUTn1iQPdnV4UXJahdClEAzDUEBHRkPm9QZTPzYFKlYgnN2JLIv6EePqJiIiGrKjCgmP7muHjZd1jXiKefmKoISKiIdPo1CibaUPjkU6lSyEagKGGiIiGRVJJMKfp0Xi0Q+lS6CJ4+omIiGgIrIXJUGvVcDl6lC6FBiEHZUgJ2O9pVKFm48aNkCQJq1evDi371a9+hfnz5yMlJQWSJKGzs/OS23n00UchSVK/W3l5+WhKIyKiCAv6g5wHaoxKxMksgVGEmsrKSmzevBlTpkzpt9zj8WDJkiV44IEHhrW9yy+/HE1NTaHbP/7xj5GWRkREUZCUYUDLyS52Gh6Det1+GBJwOosRxbju7m6sWLECzz//PNavX9/vubOtNrt27RpeIRoNsrOzR1IOEREpIMViRFK6AQ2H2lF4uUXpcugcve4ALLlmpcuIuhG11Nx9991YtmwZFi5cGLZCjh49itzcXJSWlmLFihU4derUBdf1er1wuVz9bkREFH1CCAT8Mo5XtypdCp3D3xuA1qBWuoyoG3ao2bp1K6qqqrBhw4awFTF79my8+OKL2L59O5599lnU1dXhC1/4Arq6ugZdf8OGDUhNTQ3dCgoKwlYLERENnVqtQuk0K9RaXncylggk3gzdwDBPP9XX1+Pee+/Fjh07YDAYwlbE0qVLQ/enTJmC2bNno6ioCL///e9x++23D1h/7dq1WLNmTeixy+VisCEiUpAICpw42AY5KCCpJEgALPlJSM4I33cF0aUMK9Ts27cPLS0tmDFjRmhZMBjE7t278fTTT8Pr9UKtHn1zV1paGiZMmIBjx44N+rxer4derx/1foiIKDyKp2RCiL4roSRJghACdR+1MdQoJPHaaPoMq71wwYIFOHjwIKqrq0O3WbNmYcWKFaiurg5LoAH6OiLX1tYiJycnLNsjIqLIOzskx9n7mQVJOPKBHQF/UOHKEk+iXmg/rJaa5ORkVFRU9FtmNpthsVhCy+12O+x2e6iV5eDBg0hOTkZhYSEyMvpmdV2wYAFuuOEG3HPPPQCAH/zgB7j22mtRVFSExsZGPPLII1Cr1bjllltG/QaJiEgZKRYjmutcaDnZBX9vECqNhPyJ6fD1BNBU60ThpAyo1OyLQ+ET9pF5nnvuOaxbty70+KqrrgIAbNmyBbfddhsAoLa2Fm1tbaF1GhoacMstt8DhcMBqtWLevHl4//33YbVaw10eERFFUd6EdKjUEgxmLbweP2qrWiFJQMFlGWiqdSJvQjoAoLPFA68nAFtxisIVx4dEPf0kibMnQWOYy+VCamoqnE4nUlL4D4KIKBY01HQg4AtCkiSYUnXwOH0oquB4N+Fw8hMHimJg7KBwf38n3hjKREQ0JuRPTO/3+LiDY93Q6PBkJhERjQlavRonP3YgDk4gKCqRjx9DDRERjQkFl2UguzQFDYc7lC4lpvl7g9AbE/NEDEMNERGNGXqTFgazFk3HOpUuJWZ5unwwJCXeZJYAQw0REY0x1sJkOFt7lC4jZvV0+WFM1ildhiIYaoiIaMzJm5iO4/tb0dnsUbqUmOPvDUCXgJNZAgw1REQ0BiVnGFA63YpgQEbt/hZ4XD6lS4oZiTqZJcBQQ0REY5glLwml06zobPHgeHUr/D5OuUAXlpjdo4mIKGZIkoTc8WmQZYGGQ+0AgPzLMqBSJWZrxKUk8lFhqCEiopigUkkovNwCvzeIEwfaYEzSIntcasKearmQxB2lhqefiIgoxmj1apROsyLFasTx6lZ02N1KlzSmaHXqhO2DxFBDREQxyZyqx7jpWRAyULu/BT1diflFfr7csjScPtKRkCMLM9QQEVFMy8g1o3SaFR3NHtQdaEPQLytdkuLyy9PRUJN4IzOzTw0REcW8s52Jg0EZ9Z+2Q6tTI3dCWsL2tzEm6aDWqNDd4UVSul7pcqKGLTVERBQ31GoViidnwpKfhLrqNjgau5UuSTG549PQVNuZUKehGGqIiCjuGMxalE63QpIk1Fa1wNcTULokRRRebsGpT9uVLiNqGGqIiChuZeSYUTrdiuY6FxoOtydUqwUA6I0a6E0auNoSYy4thhoiIoprkiShYFIGMguScXx/Yl0C3nqqCx6nD3pzYszazVBDREQJwWDWYtyMLMhBgdr98X1KqrPFg+P7W6E3a1A6zQq9MTGuC0qMd0lERHSGJS8JGTlmNBzuQDAoo+CyDKg18fE3fk+XD43HOpFqNaF0ulXpcqKOoYaIiBKOpOo7JRX0y6g/1A61WoX88nRIMTqflN8XRP2n7X0dpKdZE/ZSdoYaIiJKWGpt3yXgvt4AThxsg96kRc742JlP6uwknwJA8WQLVOr4aHEaKYYaIiJKeDqDBiVTrejp9qHuozYkpeuRVZSidFkXZT/uhMflQ355OnQGfp0DDDVEREQhxiQdSqdZ0dXei+PVrUjJNCIzP0npsvrpsLvR3uRGdkkqsktTlS5nTGGoISIiOk9yhgHJGQY4Wz04Xt2KNJsJGTlmRWvyuHxoOtaJNJsJ46ZnKVrLWMVQQ0REdAGpVhNSrSZ02N04Xt2KjFwz0rJMUa0h4Avi1KftMCR9NkoyDY6hhoiI6BLSs81IzzbDcbobx6tbYStOgTktshNFCiFwcNdpqDUSLrsyJ+E7AQ8FjxAREdEQWfKS+vrcdPT1ufF6/BHb19HKZtiKU5BiMcJxOnFGQR4NttQQERENU3ZJKoQQOH2kE35vEAWXpUOjVY9qm53NHnQ2e5BmM0GWBayFyZCDAkf3OZCUpoe1MDlM1ccvhhoiIqIRkCQJ+RPTEQzKaDjUAUkC8i/LgGqYA/idrumALAQ8nV5MnJODxqMd6GzpwaTP5wIA5n2lLBLlxyWGGiIiolFQq1UoqrDA7w3i5ME26E0a5IxPu2CHXjkoA5IEERT46O/1yJuQjhSLAcbyDABAzvg09p8ZoVEdtY0bN0KSJKxevTq07Fe/+hXmz5+PlJQUSJKEzs7OIW3rmWeeQXFxMQwGA2bPno0PPvhgNKURERFFlVavRslUK9JzzKirbkPLSRcAwN3pheN0N4IBGcerW1Fb1YrWU12wH3di8hfzYStOgTFZF9qOJEkcf2aERhxqKisrsXnzZkyZMqXfco/HgyVLluCBBx4Y8rZefvllrFmzBo888giqqqowdepULF68GC0tLSMtj4iISBHGJB1Kp1thSNKidn8L6j5qRYfdg6OVzSi63IKCSRnoaHIj4Jeh1Y+uHw71N6JQ093djRUrVuD5559Henp6v+dWr16NH/3oR5gzZ86Qt/ef//mfuOOOO7By5UpMmjQJzz33HEwmE1544YWRlEdERKS4FIsR46ZnIW9iOkqnW1FUYYFaq0J3Ry9kWaCowqJ0iXFnRKHm7rvvxrJly7Bw4cJRF+Dz+bBv375+21KpVFi4cCH27Nkz6Gu8Xi9cLle/GxER0ViUnm2GSiWFTjFp9WqYUnSXeBWNxLA7Cm/duhVVVVWorKwMSwFtbW0IBoOw2Wz9lttsNhw+fHjQ12zYsAHr1q0Ly/6JiIii6ewoxRR+w2qpqa+vx7333ovf/va3MBgMkarpktauXQun0xm61dfXK1YLERERjQ3DaqnZt28fWlpaMGPGjNCyYDCI3bt34+mnn4bX64VaPbxOT5mZmVCr1Whubu63vLm5GdnZ2YO+Rq/XQ6+P7PDUREREFFuG1VKzYMECHDx4ENXV1aHbrFmzsGLFClRXVw870ACATqfDzJkzsXPnztAyWZaxc+dOzJ07d9jbIyIiosQ0rJaa5ORkVFRU9FtmNpthsVhCy+12O+x2O44dOwYAOHjwIJKTk1FYWIiMjL6BhRYsWIAbbrgB99xzDwBgzZo1uPXWWzFr1ixcccUV2LRpE9xuN1auXDnqN0hERESJIewjCj/33HP9OvFeddVVAIAtW7bgtttuAwDU1taira0ttM7XvvY1tLa24uGHH4bdbse0adOwffv2AZ2HiYiIiC5EEkIIpYsYLZfLhdTUVDidTqSkpChdDhEREQ1BuL+/ObkEERERxQWGGiIiIooLDDVEREQUFxhqiIiIKC4w1BAREVFcYKghIiKiuMBQQ0RERHGBoYaIiIjiQthHFFbC2fEDXS6XwpUQERHRUJ393g7XOMBxEWq6uroAAAUFBQpXQkRERMPV1dWF1NTUUW8nLqZJkGUZjY2NSE5OhiRJ/Z5zuVwoKChAfX09p1A4g8dkIB6TgXhMBuIxGYjHZCAek8ENdlyEEOjq6kJubi5UqtH3iImLlhqVSoX8/PyLrpOSksIP13l4TAbiMRmIx2QgHpOBeEwG4jEZ3PnHJRwtNGexozARERHFBYYaIiIiigtxH2r0ej0eeeQR6PV6pUsZM3hMBuIxGYjHZCAek4F4TAbiMRlcNI5LXHQUJiIiIor7lhoiIiJKDAw1REREFBcYaoiIiCguMNQQERFRXIjbUHPkyBEsX74cmZmZSElJwbx58/D3v/+93zqnTp3CsmXLYDKZkJWVhR/+8IcIBAIKVRx5u3btgiRJg94qKytD6/3+97/HtGnTYDKZUFRUhP/4j/9QsOrIGuoxefPNNzFnzhwkJyfDarXipptuwokTJ5QrPIKGckweffTRQZ83m80KVx8ZQ/2cCCHw05/+FBMmTIBer0deXh6eeOIJBSuPnKEckxMnTgz6/Pvvv69w9ZEx1M/JWceOHUNycjLS0tKiX2wUDeW41NTU4Oqrr4bNZoPBYEBpaSkefPBB+P3+4e1MxKmysjJxzTXXiI8++kgcOXJE/Nu//ZswmUyiqalJCCFEIBAQFRUVYuHChWL//v3ijTfeEJmZmWLt2rUKVx45Xq9XNDU19bt9+9vfFiUlJUKWZSGEEG+88YbQaDTi2WefFbW1teIvf/mLyMnJEb/4xS8Urj4yhnJMjh8/LvR6vVi7dq04duyY2Ldvn7jqqqvE9OnTFa4+MoZyTLq6ugasM2nSJHHrrbcqW3yEDOWYCCHEd7/7XTFx4kTx+uuvi+PHj4sPP/xQ/O1vf1Ow8sgZyjGpq6sTAMRbb73Vbz2fz6dw9ZEx1M+JEEL4fD4xa9YssXTpUpGamqpMwVEylONSW1srXnjhBVFdXS1OnDghXn/9dZGVlTXs7+S4DDWtra0CgNi9e3domcvlEgDEjh07hBB9X94qlUrY7fbQOs8++6xISUkRXq836jUrwefzCavVKh577LHQsltuuUV85Stf6bfeU089JfLz8wf8o4xHgx2TV155RWg0GhEMBkPLtm3bJiRJittfzuca7Jicr7q6esC/uXg22DH59NNPhUajEYcPH1awMuUMdkzOhpr9+/crV5iCLvZv57777hPf+MY3xJYtW+I+1JxvKL9ThBDi+9//vpg3b96wth2Xp58sFgsmTpyIl156CW63G4FAAJs3b0ZWVhZmzpwJANizZw8mT54Mm80Wet3ixYvhcrnwySefKFV6VG3btg0OhwMrV64MLfN6vTAYDP3WMxqNaGhowMmTJ6NdYtQNdkxmzpwJlUqFLVu2IBgMwul04te//jUWLlwIrVarYLXRMdgxOd9//dd/YcKECfjCF74QxcqUM9gx+fOf/4zS0lL85S9/QUlJCYqLi/Htb38b7e3tClYaPRf7nFx33XXIysrCvHnzsG3bNgWqU8aFjsnbb7+NV155Bc8884xClSlrKL9Tjh07hu3bt+OLX/zi8DY+mrQ1ltXX14uZM2cKSZKEWq0WOTk5oqqqKvT8HXfcIRYtWtTvNW63WwAQb7zxRrTLVcTSpUvF0qVL+y3bvHmzMJlM4q233hLBYFDU1NSI8vJyAUC89957ClUaPYMdEyGE2LVrl8jKyhJqtVoAEHPnzhUdHR3RL1ABFzomZ/X09Ij09HTxk5/8JIpVKWuwY3LnnXcKvV4vZs+eLXbv3i3+/ve/i2nTpomrr75aoSqja7Bj0traKn72s5+J999/X3zwwQfi/vvvF5Ikiddff12hKqNrsGPS1tYmCgoKxDvvvCOEEAnZUnOx3ylz584Ver1eABCrVq3q10I+FDEVau6//34B4KK3Q4cOCVmWxXXXXSeWLl0q/vGPf4h9+/aJu+66S+Tl5YnGxkYhRHyFmqEel3PV19cLlUol/vCHP/RbLsuyuO+++4TBYBBqtVqkp6eLRx99VAAQ77//fjTf1qiE85g0NTWJsrIy8cMf/lBUVVWJd955R3zxi18UCxYsiKlTcuE8Juf63e9+JzQaTb9TubEinMfkjjvuEABETU1NaNm+ffsEgJg6JRWpz8lZ3/zmN4d9SkFp4TwmN9xwg7j//vtDj2M51ETis3Lq1CnxySefiN/97nciLy9v2H8sxdQ0Ca2trXA4HBddp7S0FO+++y4WLVqEjo6OftObl5WV4fbbb8ePfvQjPPzww9i2bRuqq6tDz9fV1aG0tBRVVVWYPn16pN5G2A31uOh0utDjxx9/HL/4xS9w+vTpQU+hBINB2O12WK1W7Ny5E9dccw1aWlpgtVrDXn8khPOYPPTQQ9i+fXu/qxcaGhpQUFCAPXv2YM6cOeF/AxEQic8JACxYsAApKSl49dVXw1pvNITzmDzyyCP48Y9/3O9qjZ6eHphMJvztb3/Dl7/85fC/gQiI1OfkrGeeeQbr169HU1NTWOqNhnAek7S0NHR3d4ceCyEgyzLUajV+9atf4V//9V/D/wYiJNKfld/85jdYtWoVurq6oFarh1STZkhrjRFWq3VIX6oejwcAoFL17zKkUqkgyzIAYO7cuXjiiSfQ0tKCrKwsAMCOHTuQkpKCSZMmhbnyyBrqcTlLCIEtW7bgW9/61gU/VGq1Gnl5eQCA//3f/8XcuXNjJtAA4T0mHo9nwGfp7D+ws5+nWBCJz0ldXR3+/ve/x2w/iXAek89//vMIBAKora3FuHHjAPQNLQEARUVF4Ss6wiLxOTlXdXU1cnJyRlNi1IXzmOzZswfBYDD0+PXXX8dPfvITvPfee6HfubEi0p8VWZbh9/tDoW+oO4k7ra2twmKxiBtvvFFUV1eLmpoa8YMf/EBotVpRXV0thPjsku5FixaJ6upqsX37dmG1WuP6ku6z3nrrrUGbBYXoO3bPPvusOHTokNi/f7/43ve+JwwGg9i7d68ClUbPxY7Jzp07hSRJYt26deLIkSNi3759YvHixaKoqEh4PB4Fqo2Oix2Tsx588EGRm5srAoFAFCtTzsWOSTAYFDNmzBBXXXWVqKqqEh9++KGYPXu2+PKXv6xApdFzsWPy4osvit/97nfi0KFD4tChQ+KJJ54QKpVKvPDCCwpUGj1D+bdzViyffhquix2X3/zmN+Lll18Wn376qaitrRUvv/yyyM3NFStWrBjWPuIy1AghRGVlpVi0aJHIyMgQycnJYs6cOQP6ypw4cUIsXbpUGI1GkZmZKf793/9d+P1+hSqOnltuuUVceeWVgz7X2toq5syZI8xmszCZTGLBggUx1ZdmpC52TIQQ4n//93/F9OnThdlsFlarVVx33XVD+oUVyy51TILBoMjPzxcPPPBAFKtS1qWOyenTp8WNN94okpKShM1mE7fddptwOBxRrDD6LnZMXnzxRXHZZZcJk8kkUlJSxBVXXCFeeeWVKFcYfZf6nJwrkULNxY7L1q1bxYwZM0RSUpIwm81i0qRJ4sc//rHo6ekZ1j5iqk8NERER0YXE5Tg1RERElHgYaoiIiCguMNQQERFRXGCoISIiorjAUENERERxgaGGiIiI4gJDDREREcUFhhoiIiKKCww1REREFBcYaoiIiCguMNQQERFRXGCoISIiorjw/wFpTqMr5fe05QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aDP = float(statePop)/nDistricts\n",
    "maxCCBratio = 1./4.  #adjustable max fraction of district pop for corner county blocks.  Let's try 3/16\n",
    "maxCCBpop = maxCCBratio * aDP\n",
    "print(\"continuing from above, develop corner county blocks from each low-pop corner county until it hits\",r3(maxCCBratio),\"of\",int(aDP) )\n",
    "CCBlist, CCBpop, passThruList = list(), list(), list()  #\"waiveThru\" counties get chance to join a cluster even if high pop\n",
    "nFuses = 0\n",
    "for c in has1neighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and only 1 neighbor is less pop than\",int(maxCCBpop),\"vs districtPop\",int(aDP) )\n",
    "    if countyPop[c] > maxCCBpop:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        nc = neighborCountyList[c][0]\n",
    "        plotPoly(countyGeom[nc],0.5)\n",
    "        plotPoly(MAP,0.2)\n",
    "        print(\"county\",c,\"has pop\",countyPop[c],\"and its only neighbor has pop\",countyPop[nc],\"vs districtPop\",aDP,\". Default = keep un-fused\")\n",
    "        print(\"enter 0 to keep\",c,\"as an unfused collection of units, 1 to fuse as one unit and stop, 2 to force-add at least the next closest county\")\n",
    "        fuseChoice = input(\"0, 1, or 2.  Any other input taken as a zero\")\n",
    "        if fuseChoice == 0:\n",
    "            keepUnfused = True\n",
    "            break\n",
    "        elif int(fuseChoice) == 1:\n",
    "            CCBlist.append([c])\n",
    "            CCBpop.append(countyPop[c])  \n",
    "            hasFewNeighborCs.append(c)  #this adds this [c] to the final list of fused units, but will fail to find more in below\n",
    "        elif int(fuseChoice) == 2:\n",
    "            CCBlist.append([c,nc ] )\n",
    "            CCBpop.append(countyPop[c] + countyPop[nc])\n",
    "            hasFewNeighborCs.append(c)\n",
    "            passThruList.append(c)   #pass on thru to the below 2neighbor logic even if too high-pop to qualify\n",
    "    else:\n",
    "        hasFewNeighborCs.append(c)  #normal case; we'll handle in next, more general, for loop            \n",
    "        \n",
    "for c in hasFewNeighborCs:\n",
    "    print(\"checking if county\",c,\"with pop\",countyPop[c],\"and 1 or 2 neighbors is less pop than\",int(maxCCBpop) )\n",
    "    if countyPop[c] < maxCCBpop :\n",
    "        CCBlist.append([c])\n",
    "        CCBpop.append(countyPop[c])\n",
    "        CCBno = CCBlist.index([c])\n",
    "    if c in passThruList:\n",
    "        CCBno = CCBlist.index([c,neighborCountyList[c][0] ])\n",
    "    if countyPop[c] < maxCCBpop or c in passThruList:            \n",
    "        CCBstarter = c\n",
    "        canAdd = True\n",
    "        while canAdd:\n",
    "            nbrSet = set()\n",
    "            for cc in CCBlist[CCBno]:\n",
    "                nbrSet = ( nbrSet.union(set(neighborCountyList[cc])) ).difference(set(CCBlist[CCBno]))\n",
    "            nPop = [countyPop[cc] for cc in nbrSet]\n",
    "            nDist = [countyGeom[cc].centroid.distance(countyGeom[CCBstarter].centroid) for cc in nbrSet]\n",
    "            idx = np.argsort(nDist)\n",
    "            nbrList, newList = list(nbrSet), list()\n",
    "            for i in range(len(nDist)):  #add counties, closest to starter that will fit until over\n",
    "                cc = nbrList[idx[i]]\n",
    "                if CCBpop[CCBno] + countyPop[cc] < maxCCBpop:\n",
    "                    newList.append(cc)\n",
    "                    CCBlist[CCBno].append(cc)\n",
    "                    CCBpop[CCBno] += countyPop[cc]\n",
    "                    #print(\"adding county\",cc,\"with pop\",countyPop[cc],\"to list started by\",CCBstarter,\".Cluster pop now\",CCBpop[CCBno]  )\n",
    "            if newList == list():  #no eligible neighbor counties found; stop\n",
    "                canAdd = False\n",
    "                for cc in CCBlist[CCBno]:\n",
    "                    plotPoly(countyGeom[cc])\n",
    "                    plotCenter(CCBno,countyGeom[cc])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "                    \n",
    "print(\"Below I plot these again by county no, with faded neighboring counties\")\n",
    "plotPoly(MAP,0.15)\n",
    "for L in CCBlist:\n",
    "    print(CCBlist.index(L),\" \",L)\n",
    "    for c in L:\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(c,countyGeom[c])\n",
    "        for cc in list(set(neighborCountyList[c]).difference(L) ):\n",
    "            plotPoly(countyGeom[cc],0.4)\n",
    "            plotCenter(cc,countyGeom[cc],8)\n",
    "plt.show()\n",
    "rejectList = list()\n",
    "print(\"Now finalize the corner lists.  Remember, our avgDistrictPop and normal pop threshold are\",r3(aDP), int(maxCCBpop))\n",
    "print(\"You may suggest [ [6], [9,15,16]  ] \" )\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if L[0] not in passThruList:  #if we forced the fused-counties list above, don't give option here to modify\n",
    "        print(\"let's decide whether to delete, accept, or modify list\",L,\"with pop\",CCBpop[i] )\n",
    "        isOK = input(\"enter 0 to ax, 1 to accept, or type in a [replacement list] (must start with same c as orig list)\")\n",
    "        if not (isOK == 1 or isOK == str(1)) :\n",
    "            if isOK == 0 or isOK == str(0) :\n",
    "                rejectList.append(L)\n",
    "            else:\n",
    "                notGood = True\n",
    "                while notGood:       \n",
    "                    Lnew = ast.literal_eval(isOK)        \n",
    "                    if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                        notGood = False\n",
    "                    else:\n",
    "                        print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                        isOK = input(\"Try again here \")\n",
    "                print(\"OK, I will replace\",L,\"with\",Lnew)\n",
    "                CCBpop[i] = np.sum( [countyPop[c] for c in Lnew] )\n",
    "                CCBlist[i] = Lnew.copy()\n",
    "for L in rejectList:\n",
    "    del CCBpop[ CCBlist.index(L)]\n",
    "    del CCBlist[CCBlist.index(L)]\n",
    "stillAdding = True\n",
    "while stillAdding:\n",
    "    addMore = input(\"enter 1 if you want to manually add another corner-county cluster\")\n",
    "    if int(addMore) != 1:\n",
    "        stillAdding = False\n",
    "    else:\n",
    "        isOK = input(\"Type in a [county list], where the corner county is first in the list.  Or type 0 to stop\")\n",
    "        if isOK == 0 or isOK == str(0) :\n",
    "            print(\"OK, we'll stop adding corner clusters\")\n",
    "            stillAdding = False\n",
    "        else:\n",
    "            notGood = True\n",
    "            while notGood:       \n",
    "                Lnew = ast.literal_eval(isOK)        \n",
    "                if len(list(set(Lnew).difference(set([c for c in range(nCounties) ] )))) == 0:\n",
    "                    notGood = False\n",
    "                    for L in CCBlist:\n",
    "                        if set(L).intersection(set(Lnew)) != set(list()) :\n",
    "                            notGood = True\n",
    "                            print(\"OOPS! The following inputted counties are already in\",L,\":\",set(L).intersection(set(Lnew)) )\n",
    "                            isOK = input(\"Try again here \")\n",
    "                    if notGood == False:\n",
    "                        CCBlist.append(Lnew)\n",
    "                        CCBpop.append( np.sum( [countyPop[c] for c in Lnew] ) )\n",
    "                else:\n",
    "                    print(\"Oops!  You didn't enter a valid list.  Try again as [\",L[0],\", n1, n2, ... ] where n1, n2 are county integers\")\n",
    "                    isOK = input(\"Try again here \")\n",
    "print(\"Our final pops and fused-county lists are...\")\n",
    "allFusedCounties = list()\n",
    "CCBgeom = [dummyPoly for L in CCBlist ]\n",
    "CCBcp = list()\n",
    "for i, L in enumerate(CCBlist):\n",
    "    CCBcp.append( getHDcp([countyCP[c] for c in L], [countyPop[c] for c in L], [ j for j in range(len(L)) ] ) )\n",
    "    print(CCBpop[i],L)\n",
    "    pops, CPs = list(), list()\n",
    "    for c in L:\n",
    "        if c == L[0]:\n",
    "            CCBgeom[i] = countyGeom[c]\n",
    "        else:\n",
    "            CCBgeom[i] = CCBgeom[i].union(countyGeom[c])\n",
    "        allFusedCounties.append(c)\n",
    "        pops += countyPop[c]       #  IS THIS EVEN USED?\n",
    "        CPs.append(countyCP[c])   #  IS THIS EVEN USED?\n",
    "        plotPoly(countyGeom[c])\n",
    "        plotCenter(i,countyGeom[c])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "206c9372-8ec8-4190-93ed-04df286466e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now identify the border counties + border fused units, and interior small counties with pop < 15550\n",
      "We defined a total of 1 unit (but not corner-cluster) counties in the map, excluding the cut-out districts\n"
     ]
    }
   ],
   "source": [
    "maxWholeBorderCountyPop = 0.02 * aDP   #to encourage contiguity, border counties > 0.02 aDP will be forced whole\n",
    "maxInteriorBorderCountyPop = 0.02 * aDP\n",
    "print(\"now identify the border counties + border fused units, and interior small counties with pop <\", int(maxInteriorBorderCountyPop))\n",
    "unitCounties, borderCounties = list(), list()\n",
    "origMAP = MAP\n",
    "if MAP.geom_type == dummyPoly.geom_type:\n",
    "    MAPexterior = MAP.exterior\n",
    "else:\n",
    "    maxArea = 0\n",
    "    for geo in MAP.geoms:\n",
    "        if geo.area > maxArea:\n",
    "            MAPexterior = geo.exterior\n",
    "            maxArea = geo.area\n",
    "            MAP0 = geo\n",
    "    MAP = MAP0\n",
    "for c in uncutCountyList:\n",
    "    if countyGeom[c].intersects(MAPexterior):\n",
    "        borderCounties.append(c)\n",
    "        if c not in allFusedCounties:\n",
    "            if countyPop[c] < maxWholeBorderCountyPop:\n",
    "                unitCounties.append(c)\n",
    "    else:\n",
    "        if countyPop[c] < maxInteriorBorderCountyPop and c not in allFusedCounties:\n",
    "            unitCounties.append(c)\n",
    "print(\"We defined a total of\",len(unitCounties),\"unit (but not corner-cluster) counties in the map, excluding the cut-out districts\")        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e0ff719c-59dc-4827-a522-11bd352f7524",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this state, do we have a lot of populated fragmented VTDs?\n",
      "50 fragmented VTDs out of 14191\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI, here are the locations of fragmented VTDs with pop > 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"For this state, do we have a lot of populated fragmented VTDs?\")\n",
    "nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "print(len(fragmentedVTDs),\"fragmented VTDs out of\",nVTDs)\n",
    "fragPop = [tractPop[v] for v in fragmentedVTDs]\n",
    "plt.hist(fragPop)\n",
    "plt.show()\n",
    "print(\"FYI, here are the locations of fragmented VTDs with pop > 3000\")\n",
    "for v in fragmentedVTDs:\n",
    "    if tractPop[v] > 3000:\n",
    "        plotPoly(vtdGeom[v])\n",
    "plotPoly(MAP,0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71d771f8-dbe2-48f9-a702-9f66a62d1174",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\n",
      "I split up 50 fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\n",
      "looking for surrounds, now working on vtd 0\n",
      "looking for surrounds, now working on vtd 500\n",
      "looking for surrounds, now working on vtd 1000\n",
      "looking for surrounds, now working on vtd 1770\n",
      "looking for surrounds, now working on vtd 2756\n",
      "looking for surrounds, now working on vtd 3725\n",
      "looking for surrounds, now working on vtd 4641\n",
      "looking for surrounds, now working on vtd 5142\n",
      "looking for surrounds, now working on vtd 7336\n",
      "looking for surrounds, now working on vtd 9017\n",
      "looking for surrounds, now working on vtd 9519\n",
      "looking for surrounds, now working on vtd 10023\n",
      "looking for surrounds, now working on vtd 14144\n",
      "After surround checks, we appear to have 5841 building blocks defined. We captured 37 surrounded VTDs\n",
      "working on unit neighbor lists for county 0\n",
      "working on unit neighbor lists for county 20\n",
      "working on unit neighbor lists for county 60\n",
      "All done creating unit lists\n"
     ]
    }
   ],
   "source": [
    "#3/2/24 - split up all multipoly VTDs into fragments, divvy pop by area\n",
    "unitPop, unitCP, unitGeom, nbrUnitGeom, allUnits = list(), list(), list(), list(), list()\n",
    "vtdUnits, countyUnits, borderUnits = list(),list(),list()\n",
    "countyUnitList = [list() for c in range(nCounties) ]\n",
    "surroundedVTDs, surrounders = list(), list()  #vtds that are surrounded by another vtd - problem for later enclaves, xfer their pops to surrounders\n",
    "#These surrounded VTDs don't get transferred to the unit list\n",
    "print(\"Now writing the master list of units, which may be individual vtd's (+surrounds), whole counties, or corner county clusters\")\n",
    "for c in unitCounties:\n",
    "    unitCP.append(countyCP[c])\n",
    "    unitPop.append(countyPop[c])\n",
    "    unitGeom.append(   countyGeom[c] )\n",
    "    nbrUnitGeom.append(countyGeom[c] )\n",
    "    countyUnits.append(c)\n",
    "    allUnits.append(c+0.5)  #use non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nVTDneighbors = [0 for v in range(nVTDs)] #this loop -- just checking for surrounded VTDs\n",
    "lastVTDneighbor = [-999 for v in range(nVTDs)]\n",
    "\n",
    "nFragmentedVTDs,fragmentedVTDs, coastalFragmentedVTDs = 0, list(), list()  #a prev version treated coastal separately\n",
    "fragVTDgeom, parentVTDno, fragVTDpop = vtdGeom.to_list(), [v for v in range(nVTDs)], tractPop.to_list()  #these lists will expand to include vtd fragments \n",
    "origVTDgeom = vtdGeom.copy() #we create a parallel fragVTDgeom list which grabs the 1st fragment, then append fragments to the total list\n",
    "origVTDpop = tractPop.copy()\n",
    "VTDchildren = [[v] for v in range(nVTDs)]  #the list of all fragment numbers associated with the original VTD, including ones that get surrounded\n",
    "countyFragVTDset = [set(countyTractList[c]) for c in range(nCounties)]\n",
    "for v in populatedTractList:\n",
    "    c = countyNo[v]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        if vtdGeom[v].geom_type != dummyPoly.geom_type :  #a multipoly vtd-based unit\n",
    "            nFragmentedVTDs +=1\n",
    "            fragmentedVTDs.append(v)\n",
    "            geos, areas = [geo for geo in vtdGeom[v].geoms ], [geo.area for geo in vtdGeom[v].geoms]\n",
    "            for i, geo in enumerate(geos):\n",
    "                if i == 0:\n",
    "                    fragVTDgeom[v] = geo\n",
    "                    fragVTDpop[v] = tractPop[v] * areas[i] / np.sum(areas)  #overwrite, then distribute balance below\n",
    "                else:\n",
    "                    fNo = len(fragVTDgeom)\n",
    "                    fragVTDgeom.append(geo)\n",
    "                    fragVTDpop.append( tractPop[v] * areas[i] / np.sum(areas) )\n",
    "                    parentVTDno.append(v)\n",
    "                    countyFragVTDset[c].add(fNo )\n",
    "                    VTDchildren[v].append(fNo)\n",
    "                    nVTDneighbors.append(0)\n",
    "                    lastVTDneighbor.append(-999)\n",
    "                    \n",
    "print(\"I split up\",nFragmentedVTDs,\"fragmented VTDs. Now define unit neighbor lists and fuse geometries of surrounds.\")\n",
    "\n",
    "debugV = -7616\n",
    "for kk,V in enumerate(populatedTractList):\n",
    "    if kk%500 == 0:\n",
    "        print(\"looking for surrounds, now working on vtd\",V)\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties:\n",
    "        for v in VTDchildren[V]:\n",
    "            for vv in countyFragVTDset[c].difference({v}) :\n",
    "                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                    nVTDneighbors[v] +=1\n",
    "                    lastVTDneighbor[v] = vv\n",
    "                    if v == debugV:\n",
    "                        print(\"I just added neighbor\",vv,\"to magicV\",v)\n",
    "                #if nVTDneighbors[v] >= 4:  #Enough to have >=2 even after surrounds.  Will do full neighbor list later\n",
    "                #    break\n",
    "            if nVTDneighbors[v] < 4:  #OK if a vtd has only 1 intracounty neighbor IF it touches another county, it's not surrounded\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if fragVTDgeom[v].intersects(countyGeom[cc]):\n",
    "                        if cc not in unitCounties + allFusedCounties:\n",
    "                            for vv in countyFragVTDset[cc] :\n",
    "                                if fragVTDgeom[vv].intersects(fragVTDgeom[v]):\n",
    "                                    nVTDneighbors[v] +=1\n",
    "                                    lastVTDneighbor[v] = vv\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                print(\"WARNING. vtd frag\",v,\"in county\",c,\"intersected county\",cc,\"but had no intracounty neighbors\")\n",
    "                                plotPoly(fragVTDgeom[v],2)\n",
    "                                plotCenter(\"iso\",fragVTDgeom[v])\n",
    "                                plotPoly(countyGeom[cc])\n",
    "                        else:\n",
    "                            nVTDneighbors[v] +=1\n",
    "                            if nVTDneighbors[v] == 1:\n",
    "                                raise Exception(\"neighborless vtd fragment\",v,\"neighbors a unit or fused county\",cc)                                \n",
    "            if v == debugV:\n",
    "                print(v,\"has\",nVTDneighbors[v],\"neighbors.  its last is\",lastVTDneighbor[v])\n",
    "                \n",
    "for loop in range(3): #to catch surrounds of surrounds\n",
    "    for V in populatedTractList:\n",
    "        c = countyNo[V]\n",
    "        if c not in allFusedCounties and c not in unitCounties: \n",
    "            for v in VTDchildren[V]:\n",
    "                if nVTDneighbors[v] == 1:\n",
    "                    vv = lastVTDneighbor[v]\n",
    "                    if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                        for sNo, s in enumerate(surroundedVTDs):\n",
    "                            if surrounders[sNo] == v:\n",
    "                                surrounders[sNo] = vv\n",
    "                    surroundedVTDs.append(v)\n",
    "                    surrounders.append(vv)\n",
    "                    nVTDneighbors[vv] -=1\n",
    "                    nVTDneighbors[v] -=1 #in case this surrounder becomes a later surroundee\n",
    "                    fragVTDpop[vv] += fragVTDpop[v]\n",
    "                    fragVTDpop[v] = 0\n",
    "                    if lastVTDneighbor[vv] == v:  #find another neighbor in case this surrounder is also a surroundee in loop 2\n",
    "                        for vvv in countyFragVTDset[c]:\n",
    "                            if vvv not in [v,vv]:\n",
    "                                if fragVTDgeom[vvv].intersects(fragVTDgeom[vv]):\n",
    "                                    lastVTDneighbor[vv] = vvv\n",
    "                    if lastVTDneighbor[vv] == v:\n",
    "                        print(\"WARNING.  We did not find another neighbor of surrounder\",vv,\"other than surroundee\",v)\n",
    "                    if loop > 0:\n",
    "                        print(\"On loop\",loop+1,\"for county\",c,\" we found that vtd frag\",v,\"now has only 1 neighbor\",vv)\n",
    "if STATE == \"NJ\":  #state-specific manual surround enforcement\n",
    "    c = 18\n",
    "    print(\"visualizing VTD fragments in county\",c)\n",
    "    for v in countyFragVTDset[c]:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v, fragVTDgeom[v])\n",
    "    plt.show()\n",
    "    specialSurrounded = ast.literal_eval(input(\"enter the [v1, v2, ...] list of surrounded fragVTDs from above map\"))\n",
    "    specialSurrounder = int(input(\"enter the fragVTDno that surrounds these\"))\n",
    "    #specialSurrounded = [1713, 1718]\n",
    "    #specialSurrounder = 1712\n",
    "    print(\"special for\",STATE,\", I believe that\", specialSurrounded,\"are surrounded by vtd (fragment)\",specialSurrounder,\". Here, let me show you\")\n",
    "    c = countyNo[parentVTDno[specialSurrounder]]\n",
    "    plotPoly(countyGeom[c], 0.5)\n",
    "    for v in specialSurrounded:\n",
    "        plotPoly(fragVTDgeom[v])\n",
    "        plotCenter(v,fragVTDgeom[v])\n",
    "    plotPoly(fragVTDgeom[specialSurrounder],0.2)\n",
    "    plotCenter(specialSurrounder, fragVTDgeom[specialSurrounder], 8)\n",
    "    plt.show()\n",
    "    shouldIcombine = int(input(\"enter 1 to apply the surrounding operation\"))\n",
    "    if shouldIcombine == 1:\n",
    "        for v in specialSurrounded:\n",
    "            vv = specialSurrounder\n",
    "            if v in surrounders:  #transferring surrounded of surrounds to master surrounder\n",
    "                for sNo, s in enumerate(surroundedVTDs):\n",
    "                    if surrounders[sNo] == v:\n",
    "                        surrounders[sNo] = vv\n",
    "            surroundedVTDs.append(v)\n",
    "            surrounders.append(vv)\n",
    "            nVTDneighbors[vv] -=1\n",
    "            nVTDneighbors[v] = 0 #we are capturing all surroundees (some of whom may touch other surroundees)\n",
    "            fragVTDpop[vv] += fragVTDpop[v]\n",
    "            fragVTDpop[v] = 0\n",
    "\n",
    "\n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] >1:\n",
    "                unitCP.append(fragVTDgeom[v].centroid)\n",
    "                unitGeom.append(fragVTDgeom[v])\n",
    "                unitPop.append(fragVTDpop[v])   #for now, ratio pop by area, not block decomposition\n",
    "                vtdUnits.append(v)   #if a border unit, we'll catch in below big loop, after checking for surround\n",
    "                allUnits.append(v)\n",
    "#for V in populatedTractList:\n",
    "#    c = countyNo[V]\n",
    "#    if c not in allFusedCounties and c not in unitCounties:  \n",
    "#        for v in VTDchildren[V]: \n",
    "for V in populatedTractList:\n",
    "    c = countyNo[V]\n",
    "    if c not in allFusedCounties and c not in unitCounties: \n",
    "        for v in VTDchildren[V]:\n",
    "            if nVTDneighbors[v] == 1:  #a surrounded VTD, transfer its pop to surrounder unit.  Don't bother with shifting centerpoints\n",
    "                print(\"somehow we missed 1-neighbor vtd frag\",v,\" with last vtd frag nbr\",lastVTDneighbor[v])\n",
    "            if nVTDneighbors[v] == 0 and v not in surroundedVTDs:\n",
    "                print(\"WARNING. unsurrounded vtd\",v,\"had no found neighbors\")\n",
    "\n",
    "for i, L in enumerate(CCBlist):\n",
    "    unitCP.append( CCBcp[i] )\n",
    "    unitPop.append(CCBpop[i])\n",
    "    unitGeom.append(   CCBgeom[i])\n",
    "    nbrUnitGeom.append(CCBgeom[i])\n",
    "    allUnits.append(i+0.25)  #use alt non-integers to avoid vtd and county and cluster confusion\n",
    "\n",
    "nUnits = len(allUnits)\n",
    "print(\"After surround checks, we appear to have\",nUnits,\"building blocks defined. We captured\",len(surroundedVTDs),\"surrounded VTDs\")\n",
    "\n",
    "unitNbrs = [list() for u in range(nUnits)]\n",
    "countyUnitList = [list() for c in range(nCounties)]\n",
    "for c in uncutCountyList:\n",
    "    for v in countyFragVTDset[c]:\n",
    "        if v in allUnits:\n",
    "            countyUnitList[c].append(allUnits.index(v))\n",
    "\n",
    "sketchyList = list()\n",
    "for c in uncutCountyList:\n",
    "    if c%20 == 0:\n",
    "        print(\"working on unit neighbor lists for county\",c)\n",
    "    if c not in allFusedCounties:  #see a separate loop below for cluster-cluster neighbors\n",
    "        if c in unitCounties:    \n",
    "            u = allUnits.index(c+0.5)\n",
    "            for cc in neighborCountyList[c]:\n",
    "                if cc not in allFusedCounties:\n",
    "                    if cc in unitCounties:\n",
    "                        unitNbrs[u].append(allUnits.index(cc+0.5))  #we'll catch the complement in the cc county's loop\n",
    "                    else:\n",
    "                        for uu in countyUnitList[cc] :\n",
    "                            if countyGeom[c].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu)\n",
    "                                unitNbrs[uu].append(u)\n",
    "            for i,geo in enumerate(CCBgeom):\n",
    "                if geo.intersects(countyGeom[c]):\n",
    "                    unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                    unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "        else:  #non-unit county      \n",
    "            for u in countyUnitList[c] :\n",
    "                for uu in list( set(countyUnitList[c]).difference({u}) ) :\n",
    "                    if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                        unitNbrs[u].append(uu )  #will catch complement later in this county's loop\n",
    "                for cc in neighborCountyList[c]:\n",
    "                    if cc not in allFusedCounties and cc not in unitCounties: #see an above block for handling unitCounties\n",
    "                        for uu in countyUnitList[cc]:\n",
    "                            if unitGeom[u].intersects(unitGeom[uu]):\n",
    "                                unitNbrs[u].append(uu )\n",
    "\n",
    "            for u in countyUnitList[c] :\n",
    "                for i,geo in enumerate(CCBgeom):\n",
    "                    if geo.intersects(unitGeom[u]):\n",
    "                        unitNbrs[u].append(allUnits.index(i+0.25) )\n",
    "                        unitNbrs[allUnits.index(i+0.25) ].append(u)\n",
    "                if len(unitNbrs[u]) == 0:\n",
    "                    print(\"ERROR - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has no neighbors\")\n",
    "                if len(unitNbrs[u]) == 1:  #after fragmentation, this unit appears surrounded.  Next block to confirm it has non-intracounty neighbors                       \n",
    "                    print(\"WARNING - unit\",u,\" = vtd\",allUnits[u],\"in county\", c,\"has only 1 neighbor=\",unitNbrs[u],\". Optional triage in next block\")\n",
    "                    sketchyList.append([u,unitNbrs[u][0] ] )\n",
    "\n",
    "CCBunits = CCBlist.copy()                        \n",
    "for i, geo in enumerate(CCBgeom):  #this loop: only cluster-cluster intersections.  Cluster-vtd and cluster-county neighbors already found above.\n",
    "    for ii in range(i+1,len(CCBgeom) ) :\n",
    "        if geo.intersects(CCBgeom[ii]):\n",
    "            unitNbrs[allUnits.index(i+0.25) ].append(allUnits.index(ii+0.25) ) #all CCB-county, CCB-vtd intersxns already covered\n",
    "            unitNbrs[allUnits.index(ii+0.25)].append(allUnits.index(i+0.25) )\n",
    "print(\"All done creating unit lists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "94741893-6835-45ac-9793-64cbaf4823b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sketchy unit 1173 has neighbor list [1673, 2]\n"
     ]
    }
   ],
   "source": [
    "for L in sketchyList:  #hope that some of these flagged units picked up unit-county or CCB neighbors\n",
    "    print(\"sketchy unit\",L[0], \"has neighbor list\",unitNbrs[L[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb4277b4-d6ef-4c8c-aa23-6fde227379ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if allUnits[u] - int(allUnits[u]) == 0.25:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e79873ab-0b8e-48a9-b4b0-05b3135b9cc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8378940d-4387-4188-8ae1-a62010500ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now build the border topology\n",
      "counties and clusters done, now working on (frag) vtd-based units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Now build the border topology\")\n",
    "borderUnits, borderType = list(), list()\n",
    "for c in borderCounties:\n",
    "    if c in unitCounties:\n",
    "        borderUnits.append(allUnits.index(c+0.5))\n",
    "        borderType.append(\"county\")\n",
    "for i,L in enumerate(CCBlist):\n",
    "    if CCBgeom[i].intersects(MAPexterior):  #should always be the case\n",
    "        borderUnits.append(allUnits.index(i+0.25))\n",
    "        borderType.append(\"cluster\")\n",
    "print(\"counties and clusters done, now working on (frag) vtd-based units\")\n",
    "for c in borderCounties:\n",
    "    if c not in unitCounties + allFusedCounties:\n",
    "        for u in countyUnitList[c]:\n",
    "            if unitGeom[u].intersects(MAPexterior):\n",
    "                borderUnits.append(u)\n",
    "                borderType.append(\"vtd\")                \n",
    "    \n",
    "for i,b in enumerate(borderUnits):\n",
    "    plotPoly(unitGeom[b])\n",
    "    if borderType[i] == \"cluster\":\n",
    "        j = int(allUnits[b])\n",
    "        for c in CCBlist[j]:\n",
    "            plotPoly(countyGeom[c],0.2)\n",
    "            plotCenter(\"fused\",countyGeom[c], 6)\n",
    "for c in unitCounties:\n",
    "    plotPoly(countyGeom[c],0.4)\n",
    "    plotCenter(\"unit\",countyGeom[c],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "937d923d-9e95-45e9-9aef-77cb4377781d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking that the list of borderUnits is contiguous all around\n",
      "number of border units = 198\n"
     ]
    }
   ],
   "source": [
    "print(\"checking that the list of borderUnits is contiguous all around\")\n",
    "print(\"number of border units =\",len(borderUnits))\n",
    "for u in borderUnits:\n",
    "    isUnbroken, smallPieceList = isContiguous(list(set(borderUnits).difference({u})),unitNbrs)\n",
    "    if not isUnbroken:\n",
    "        print(\"border is discontig if we skip\",u)\n",
    "        for uu in smallPieceList:\n",
    "            plotPoly(unitGeom[uu],0.5)\n",
    "            plotCenter(\"n\"+str(uu),unitGeom[uu])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        plotPoly(unitGeom[u])\n",
    "        plt.show()\n",
    "borderSet = set(borderUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "93603f77-77fa-4453-b54b-5ea07fcb6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here, we identify 'border' units that aren't needed to define the map border\n",
      "Bueno! no 'border units' that could be eliminated from the border set\n"
     ]
    }
   ],
   "source": [
    "print(\"here, we identify 'border' units that aren't needed to define the map border\")\n",
    "canBeRemoved, powerNeighbors = set(), set()\n",
    "for u in borderUnits:\n",
    "    uNeighbors = list(borderSet.intersection(set(unitNbrs[u])) )\n",
    "    if len(uNeighbors) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        uu = uNeighbors[0]\n",
    "        otherBorderNeighbors = set(unitNbrs[uu]).intersection(borderSet).difference({u})\n",
    "        if len(otherBorderNeighbors) > 1:  #this neighbor of our 1-border-neighbor border unit makes a border chain without the problem border unit\n",
    "            canBeRemoved.add(u)\n",
    "            powerNeighbors.add(uu)\n",
    "        plotCenter(uu,unitGeom[uu],6)\n",
    "        plotPoly(unitGeom[uu],0.5)\n",
    "        for uuu in set(unitNbrs[uu]).intersection(borderSet).difference({u}):\n",
    "            plotPoly(unitGeom[uuu])\n",
    "            plotCenter(str(uuu)+\"=N of\"+str(uu),unitGeom[uuu])\n",
    "        for uuu in set(unitNbrs[u]).difference(borderSet):\n",
    "                plotCenter(uuu+0.1,unitGeom[uuu],6)\n",
    "                plotPoly(unitGeom[uuu],0.1)\n",
    "        c = countyNo[allUnits[u]]\n",
    "        #plotPoly(countyGeom[c] )\n",
    "        #plotCenter(c, countyGeom[c])\n",
    "        plt.show()\n",
    "if len(canBeRemoved) == 0:\n",
    "    print(\"Bueno! no 'border units' that could be eliminated from the border set\")\n",
    "else:\n",
    "    print(canBeRemoved,\"is the list of 1-neighbor 'border' units that can be eliminated from the border set\")\n",
    "    print(\"... as they are enveloped on the border; the enveloping border unit has two other map-border neighbors\")\n",
    "    yesRemove = input(\"enter 1 to remove these from the list of border units\")\n",
    "    if int(yesRemove) == 1:\n",
    "        canRemove = True\n",
    "        for uu in powerNeighbors:\n",
    "            testSet = (borderSet.difference(canBeRemoved)).difference({uu})\n",
    "            if not isContiguous(list(testSet),unitNbrs):\n",
    "                canRemove = False\n",
    "                print(\"We can't complete the border if unit\",uu,\"is not included in the ring\")\n",
    "        if canRemove:\n",
    "            borderSet = borderSet.difference(canBeRemoved)\n",
    "            borderList = list(borderSet)\n",
    "            borderUnits = list(borderSet)\n",
    "            print(\"Success! we removed\",canBeRemoved,\"from the list of borderUnits\")\n",
    "    else:\n",
    "        print(\"Fine, be that way.  Sheesh, I will keep them.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b13c68e3-33ed-48b3-acca-78012ce0bf61",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the final border set\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "print(\"here is the final border set\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "173bb175-312b-4d08-a5b5-80a1d5d70416",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(isContiguous(borderUnits,unitNbrs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cfda5894-5882-4737-9494-e62ecfe794b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "STATE=\"NYupper\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fc1ae6e1-ef52-412b-a966-e9739071af8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For safekeeping, write the unit topology to a file\n"
     ]
    }
   ],
   "source": [
    "date_ = \"21Apr24\"\n",
    "print(\"For safekeeping, write the unit topology to a file\")  \n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        #for i,uu in enumerate(surrounders):  #no longer tracked\n",
    "         #   if u == uu:\n",
    "         #       unitVTDlist[u].append(surroundedVTDs[i])\n",
    "                \n",
    "unitNo = [u for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"neighborList\":unitNbrs, \"unitVTDlist\":unitVTDlist, \"unitParentVTDno\": unitParentVTDno, \"allUnits\":allUnits} )\n",
    "outname = STATE+\"unitTopologies_\"+date_+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4baaea4-7af4-4407-aeb3-f4291d335f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "vtdGeom = tractGeom.copy()  #optional reset if we need to rerun the below clipPoly routine with alternate clipPolys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "abdd07e7-f9be-480a-9d3d-b3948eeaaa2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "toSquishCountiesList = list()   #default; if we did not have any clipPoly's to move in\n",
    "nClips = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4d5fdc3c-6126-4fa8-b484-1d0052c70aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for some states like FL, MD, MN, NC we move in partial counties via clipPoly's before running option 2\n",
      "adding code here to 'push in' state panhandles ...\n",
      "performing squish no 1 for state NYupper\n",
      "clipPoly 0 intersects [4, 6, 14, 31] counties and a total of 142 units\n",
      "Here is the original cutLine and an alternate based on cut shapes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are your orig (faint) and squished shapes for clip no 1 out of 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 when ready 1\n"
     ]
    }
   ],
   "source": [
    "#starting with MN, we moved the \"option2\" block earlier in the notebook, as we run it before running option 1 (as in other states)\n",
    "print(\"for some states like FL, MD, MN, NC we move in partial counties via clipPoly's before running option 2\")\n",
    "vtdCP = tractCP.copy()\n",
    "#CODE TO SQUISH GEOMETRIES FROM CLIP LINES INTO THE MAP\n",
    "print(\"adding code here to 'push in' state panhandles ...\")\n",
    "trimDF = pd.read_csv(\"state_map_files/cutNclipPolyLists.csv\")\n",
    "stateRows = trimDF[\"state\"].to_list()\n",
    "DFrow = stateRows.index(STATE)\n",
    "nClips = int(trimDF[\"nClipPoly\"][DFrow])\n",
    "nCuts  = int(trimDF[\"nCutPoly\"][DFrow])\n",
    "#Adding in here trimming ops\n",
    "toSquishUnitsList = list()\n",
    "toSquishGeomsList = list()  #combined list of geoms we will squish (over all squish operations)\n",
    "toSquishCountiesList = list()\n",
    "squishedShapesList = list()   #unit shapes after squishing, list over all squish operations\n",
    "if nClips > 0:\n",
    "    clipPoints = ast.literal_eval(trimDF[\"clipPoints\"][DFrow])   #sets of four points\n",
    "    farPoints  = ast.literal_eval(trimDF[\"farPoints\"][DFrow])    #pairs of points\n",
    "    newFarPoints  = ast.literal_eval(trimDF[\"newFarPoints\"][DFrow])   #pairs\n",
    "    cPts = [Point(clipPoints[i][0],clipPoints[i][1]) for i in range(len(clipPoints)) ]\n",
    "    fPts = [Point(farPoints[i][0],farPoints[i][1]) for i in range(len(farPoints)) ]\n",
    "    nfPts = [Point(newFarPoints[i][0],newFarPoints[i][1]) for i in range(len(newFarPoints)) ]\n",
    "    \n",
    "    for clipNo in range(nClips):\n",
    "        print(\"performing squish no\",clipNo+1,\"for state\",STATE)\n",
    "        n0,n1,n2,n3 = int(4*clipNo), int(4*clipNo+1), int(4*clipNo+2), int(4*clipNo+3)\n",
    "        clipPoly = Polygon([cPts[n0],cPts[n1],cPts[n2],cPts[n3] ] )\n",
    "        cutLine = LineString([cPts[n0],cPts[n1]] )\n",
    "        farLine = LineString([fPts[int(2*clipNo)],fPts[int(2*clipNo+1)] ] )\n",
    "        newFarLine = LineString([nfPts[int(2*clipNo)],nfPts[int(2*clipNo+1)] ])\n",
    "        \n",
    "        toSquishCounties = list()\n",
    "        toSquishUnits = list()\n",
    "        for c in range(nCounties):\n",
    "            if clipPoly.intersects(countyGeom[c]):\n",
    "                toSquishCounties.append(c)\n",
    "                if clipPoly.contains(countyGeom[c]):\n",
    "                    toSquishUnits = toSquishUnits + countyTractList[c]\n",
    "                else:\n",
    "                    for t in countyTractList[c]:\n",
    "                        if clipPoly.contains(vtdCP[t]):\n",
    "                            toSquishUnits.append(t)\n",
    "        print(\"clipPoly\",clipNo,\"intersects\",toSquishCounties,\"counties and a total of\",len(toSquishUnits),\"units\")\n",
    "        toSquishGeomUnion = vtdGeom[toSquishUnits[0]]\n",
    "        toSquishGeoms = list()\n",
    "        for t in toSquishUnits:\n",
    "            toSquishGeomUnion = toSquishGeomUnion.union(vtdGeom[t])\n",
    "            toSquishGeoms.append(vtdGeom[t])\n",
    "        unclippedGeom = MAP.difference(toSquishGeomUnion)\n",
    "        altCutLine = toSquishGeomUnion.buffer(0.001).intersection(unclippedGeom)\n",
    "        print(\"Here is the original cutLine and an alternate based on cut shapes\")\n",
    "        #plotPoly(MAP,0.1)\n",
    "        plotPoly(cutLine.buffer(0.02))\n",
    "        plotPoly(altCutLine,0.1)\n",
    "        plt.show()\n",
    "        # could use altCutLine for a more accurate squish at the squish-no_squish boundary ...\n",
    "        #newShapes = squish(clippedGeomList, altCutLine, farLine, newFarLine)\n",
    "        squishedShapes = squish(toSquishGeoms, cutLine, farLine, newFarLine) #..but may distort results\n",
    "        toSquishUnitsList.append(toSquishUnits)\n",
    "        toSquishGeomsList.append(toSquishGeoms)\n",
    "        toSquishCountiesList.append(toSquishCounties)\n",
    "        squishedShapesList.append(squishedShapes)\n",
    "        for geo in toSquishGeoms:\n",
    "            plotPoly(geo,0.1)\n",
    "            plotPoly(geo.centroid.buffer(0.004),0.1)\n",
    "        for geo in squishedShapes:\n",
    "            plotPoly(geo)\n",
    "            plotPoly(geo.centroid.buffer(0.002),0.4)\n",
    "        print(\"These are your orig (faint) and squished shapes for clip no\",clipNo+1,\"out of\",nClips)\n",
    "        plt.show()\n",
    "        pause = input(\"enter 1 when ready\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62667c48-6c66-4381-b01d-518af61651bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now apply the squish to all impacted vtds.  \"tractGeom\" will retain original vtd shapes, so we can reset if needed\n",
    "for i, vList in enumerate(toSquishUnitsList):\n",
    "    squishedShapes = squishedShapesList[i]\n",
    "    for j, v in enumerate(vList):\n",
    "        vtdGeom[v] = squishedShapes[j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7b967d22-41fb-454f-ae47-d72d712daa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(nTracts):  #optional full state visualization after squish\n",
    "    if t not in allCutVTDs and t not in skipList:\n",
    "        plotPoly(vtdGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9715564f-3b60-43e5-9f1f-02f3dcee3933",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 22  #OPTIONAL: pick a county for visualization if desired\n",
    "print(\"here are the faint(original) and squished shapes for county\",c)\n",
    "for t in countyTractList[c]:\n",
    "    plotPoly(vtdGeom[t])\n",
    "    plotPoly(tractGeom[t],0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760bc48e-0236-4ad7-b3b8-d74795009f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"alternate simple approach for Florida Keys - translate all toward county CP\")\n",
    "c = 43\n",
    "vtdGeom = tractGeom.copy()  #a reset\n",
    "nTranslated = 0\n",
    "newCP = Point(-81, 25.1)\n",
    "for v in countyTractList[c]:\n",
    "    if clipPoly.contains(tractCP[v]):\n",
    "        xDelta, yDelta = newCP.x - tractCP[v].x, newCP.y - tractCP[v].y\n",
    "        xOFF, yOFF = 0.95 * xDelta, 0.95 * yDelta\n",
    "        vtdGeom[v] = translate(vtdGeom[v],xoff= xOFF, yoff=yOFF)\n",
    "        nTranslated +=1\n",
    "print(\"I translated\",nTranslated,\"vtds toward the center of county\",c)\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]\n",
    "countyGeom[c] = vtdGeom[countyTractList[c][0]]\n",
    "for v in countyTractList[c]:\n",
    "    countyGeom[c] = countyGeom[c].union(vtdGeom[v])\n",
    "    plotPoly(hdCP[v].buffer(0.03))\n",
    "plotPoly(countyGeom[c])\n",
    "unsquishedMAP = MAP\n",
    "MAP = countyGeom[uncutCountyList[0]]\n",
    "for c in uncutCountyList:\n",
    "    MAP = MAP.union(countyGeom[c])\n",
    "#plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4ef91f8f-dabb-4c58-a8a4-bba8e725e586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for most states to use uncut counties 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on confirming HD center 0 is inside the map's convex hull\n",
      "working on confirming HD center 500 is inside the map's convex hull\n",
      "working on confirming HD center 1000 is inside the map's convex hull\n",
      "working on confirming HD center 1770 is inside the map's convex hull\n",
      "working on confirming HD center 2756 is inside the map's convex hull\n",
      "working on confirming HD center 3725 is inside the map's convex hull\n",
      "working on confirming HD center 4641 is inside the map's convex hull\n",
      "working on confirming HD center 5142 is inside the map's convex hull\n",
      "working on confirming HD center 7336 is inside the map's convex hull\n",
      "working on confirming HD center 9017 is inside the map's convex hull\n",
      "working on confirming HD center 9519 is inside the map's convex hull\n",
      "working on confirming HD center 10023 is inside the map's convex hull\n",
      "working on confirming HD center 14144 is inside the map's convex hull\n",
      "Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note the MAP convex hull.  Overwrite with your own polygon if desired.\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 - draw polygonal HDs.  #REQUIRES CONSISTENT TREATMENT OF CCB'S \n",
    "#For GA, MA, MD, MN, NC we draw HDs for ALL vtds, even if they are in a CCB.  We use the clipPoly's to squish in the corners for these HD shapes\n",
    "#Not sure if below would work if we have cut districts AND squished-in corners\n",
    "#We normally draw for each (squished or orig) vtd.  Or, read in tract or blockgroup geometries, use those pops and unit centerpoints\n",
    "\n",
    "hdCP = [vtdGeom[v].centroid for v in range(nTracts)]  #remember, this is after any squish operation\n",
    "print(\"Here we compute the map's convex hull. We can build out of whole counties or after squish operations.\")\n",
    "useSquish = int(input(\"enter 1 to use shifted shapes (e.g. for MD, MN), else enter 0 for simple states to use uncut counties\"))\n",
    "if useSquish == 1:\n",
    "    convexMAP = hdCP[countyTractList[uncutCountyList[0]][0]].buffer(0.1) \n",
    "    for v in range(nTracts):\n",
    "        if v not in skipList and v not in allCutVTDs:\n",
    "            convexMAP = convexMAP.union(hdCP[v].buffer(0.1))\n",
    "else:  #build the map via simple union of counties not wholly in fully cut-out districts\n",
    "    convexMAP = countyGeom[uncutCountyList[0]]\n",
    "    for c in uncutCountyList:\n",
    "        convexMAP = convexMAP.union(countyGeom[c])\n",
    "    #convexMAP = MAP.centroid\n",
    "    #for c in range(nCounties):\n",
    "    #    if c not in allFusedCounties:\n",
    "    #        convexMAP = convexMAP.union(countyGeom[c])\n",
    "MAPhull = convexMAP.convex_hull.buffer(0.01*convexMAP.area**0.5)\n",
    "plotPoly(MAPhull)\n",
    "popHDlist = list()\n",
    "for t in range(nTracts):\n",
    "    if t not in allCutVTDs and t not in skipList:  #keep low-pop tracts as VEST may have assigned voters to them\n",
    "        popHDlist.append(t)\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on confirming HD center\",t,\"is inside the map's convex hull\")\n",
    "    if hdCP[t].disjoint(convexMAP.convex_hull):\n",
    "        plotPoly(hdCP[t].buffer(0.03))\n",
    "        hdCP[t] = nearest_points(convexMAP.convex_hull,hdCP[t])[0]\n",
    "        plotCenter(\"m\",hdCP[t])\n",
    "    else:\n",
    "        plotPoly(hdCP[t].buffer(0.01))\n",
    "plotPoly(convexMAP)\n",
    "plotPoly(convexMAP.convex_hull)\n",
    "plotPoly(MAPhull)\n",
    "for c in allFusedCounties:\n",
    "    plotPoly(countyGeom[c],0.2)\n",
    "print(\"Here is the convex hull and shape and a dot map of the (shifted) unit centerpoints\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Note the MAP convex hull.  Overwrite with your own polygon if desired.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8393e96a-75c6-48ec-94e8-1f6fe58d31df",
   "metadata": {},
   "outputs": [],
   "source": [
    "nHDs = nTracts  #need to run this if we start option 2 from a vest list, not a previously run HD poly list\n",
    "HDcountyNo = countyNo.copy()\n",
    "populatedTractList = popHDlist.copy()  #nomenclature equivalency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bcaf6ea2-1c3b-48c1-9ad6-937d7b7e4385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\n",
      "working on tract-tract distances for unit 0 out of 14191 . Time = 0\n",
      "working on tract-tract distances for unit 400 out of 14191 . Time = 87\n",
      "working on tract-tract distances for unit 800 out of 14191 . Time = 157\n",
      "working on tract-tract distances for unit 1200 out of 14191 . Time = 242\n",
      "working on tract-tract distances for unit 2075 out of 14191 . Time = 306\n",
      "working on tract-tract distances for unit 2756 out of 14191 . Time = 387\n",
      "working on tract-tract distances for unit 3355 out of 14191 . Time = 449\n",
      "working on tract-tract distances for unit 4441 out of 14191 . Time = 513\n",
      "working on tract-tract distances for unit 4842 out of 14191 . Time = 567\n",
      "working on tract-tract distances for unit 5246 out of 14191 . Time = 613\n",
      "working on tract-tract distances for unit 7336 out of 14191 . Time = 651\n",
      "working on tract-tract distances for unit 8917 out of 14191 . Time = 682\n",
      "working on tract-tract distances for unit 9319 out of 14191 . Time = 711\n",
      "working on tract-tract distances for unit 9721 out of 14191 . Time = 730\n",
      "working on tract-tract distances for unit 10123 out of 14191 . Time = 739\n",
      "working on tract-tract distances for unit 14144 out of 14191 . Time = 742\n",
      "All done; total elapsed  742 seconds for NYupper with 10 districts to map\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our maxD was 3.0\n"
     ]
    }
   ],
   "source": [
    "#continuing option 2 ....... ##########  THIS IS FOR TRACT - TRACT, despite the \"unit\" nomenclature\n",
    "print(\"Let us establish the (post-squish) point-point distances for all HD centers, about 8 min for 4000-unit state\")\n",
    "#NOTE: FOR LARGE STATES, RESTRICT THE SEARCH TO COUNTIES WITHIN A 3/SQRT(nDistrict) DIAMETER OF THE HOME UNIT\n",
    "uuDist = [[int(maxD+1) for t in range(nHDs)] for t in range(nHDs)] #default: too big to capture\n",
    "closeCountyList = [list() for c in range(nCounties)]\n",
    "closeCountyDist = maxD   #min(maxD,3./float(nDistricts)**0.5 * maxD )  #use above maxD for these counties as well\n",
    "xScaleSquared = xScale*xScale\n",
    "if nDistricts < 10: #closeCountyDist >= maxD:  #small state\n",
    "    closeCountyList = [[i for i in range(nCounties)] for c in range(nCounties)]  #all counties are close enough\n",
    "else:\n",
    "    for c in uncutCountyList:\n",
    "        closeCountyList[c].append(c)\n",
    "        for cc in range(c+1,nCounties):\n",
    "            if cc in uncutCountyList:\n",
    "                if cc in neighborCountyList[c]:\n",
    "                    closeCountyList[c].append(cc)\n",
    "                    closeCountyList[cc].append(c)\n",
    "                else:    \n",
    "                    dist = getLongDist(countyCP[c], countyCP[cc],xScale)\n",
    "                    if dist < closeCountyDist:\n",
    "                        closeCountyList[c].append(cc)\n",
    "                        closeCountyList[cc].append(c)\n",
    "startTime = time.time()\n",
    "\n",
    "for i,t in enumerate(populatedTractList):\n",
    "    if i %400 == 0:\n",
    "        print(\"working on tract-tract distances for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime))\n",
    "    uuDist[t][t] == 0.\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "                dist = getLongDist(hdCP[t], hdCP[tt],xScale)\n",
    "                uuDist[t][tt], uuDist[tt][t] = dist, dist\n",
    "print(\"All done; total elapsed \",int(time.time()-startTime),\"seconds for\",STATE,\"with\",nDistricts,\"districts to map\" )\n",
    "doIprint = int(input(\"enter 1 to print the uuDist histogram\"))\n",
    "if doIprint == 1:\n",
    "    plt.hist(uuDist)\n",
    "    plt.show()\n",
    "print(\"our maxD was\",maxD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f392acb-ba52-4343-80ae-a58ba0b57366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similar to above, but for angles\n",
      "working on unit-unit angles for unit 0 out of 14191 . Time = 0\n",
      "working on unit-unit angles for unit 200 out of 14191 . Time = 23\n",
      "working on unit-unit angles for unit 400 out of 14191 . Time = 43\n",
      "working on unit-unit angles for unit 600 out of 14191 . Time = 64\n",
      "working on unit-unit angles for unit 800 out of 14191 . Time = 85\n",
      "working on unit-unit angles for unit 1000 out of 14191 . Time = 109\n",
      "working on unit-unit angles for unit 1200 out of 14191 . Time = 134\n",
      "working on unit-unit angles for unit 1400 out of 14191 . Time = 151\n",
      "working on unit-unit angles for unit 2075 out of 14191 . Time = 167\n",
      "working on unit-unit angles for unit 2447 out of 14191 . Time = 185\n",
      "working on unit-unit angles for unit 2756 out of 14191 . Time = 205\n",
      "working on unit-unit angles for unit 3065 out of 14191 . Time = 224\n",
      "working on unit-unit angles for unit 3355 out of 14191 . Time = 241\n",
      "working on unit-unit angles for unit 3864 out of 14191 . Time = 259\n",
      "working on unit-unit angles for unit 4441 out of 14191 . Time = 275\n",
      "working on unit-unit angles for unit 4641 out of 14191 . Time = 290\n",
      "working on unit-unit angles for unit 4842 out of 14191 . Time = 309\n",
      "working on unit-unit angles for unit 5042 out of 14191 . Time = 321\n",
      "working on unit-unit angles for unit 5246 out of 14191 . Time = 334\n",
      "working on unit-unit angles for unit 5446 out of 14191 . Time = 344\n",
      "working on unit-unit angles for unit 7336 out of 14191 . Time = 353\n",
      "working on unit-unit angles for unit 8715 out of 14191 . Time = 360\n",
      "working on unit-unit angles for unit 8917 out of 14191 . Time = 368\n",
      "working on unit-unit angles for unit 9119 out of 14191 . Time = 376\n",
      "working on unit-unit angles for unit 9319 out of 14191 . Time = 382\n",
      "working on unit-unit angles for unit 9519 out of 14191 . Time = 387\n",
      "working on unit-unit angles for unit 9721 out of 14191 . Time = 390\n",
      "working on unit-unit angles for unit 9923 out of 14191 . Time = 392\n",
      "working on unit-unit angles for unit 10123 out of 14191 . Time = 395\n",
      "working on unit-unit angles for unit 13944 out of 14191 . Time = 396\n",
      "working on unit-unit angles for unit 14144 out of 14191 . Time = 397\n",
      "All done with angles; total elapsed seconds = 397\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 \n",
    "print(\"similar to above, but for angles\")  #convention is angle[a][b] is from a to b\n",
    "uuAngle = [[0. for t in range(nHDs)] for t in range(nHDs)]\n",
    "\n",
    "pi = 3.141592653\n",
    "twoPi = pi*2.\n",
    "startTime = time.time()\n",
    "for i, t in enumerate(populatedTractList):\n",
    "    if i %200 == 0:\n",
    "        print(\"working on unit-unit angles for unit\",t,\"out of\",nHDs,\". Time =\",int(time.time() - startTime)  )\n",
    "    for tt in populatedTractList:\n",
    "        if tt > t and (HDcountyNo[tt] in closeCountyList[HDcountyNo[t]] or HDcountyNo[t] == HDcountyNo[tt]):  #time-saver; do the triangle matrix\n",
    "            angLE = math.atan2(hdCP[tt].y - hdCP[t].y, xScale * (hdCP[tt].x - hdCP[t].x) )\n",
    "            uuAngle[t][tt], uuAngle[tt][t] = angLE % twoPi, (angLE + pi) % twoPi\n",
    "print(\"All done with angles; total elapsed seconds =\",int(time.time()-startTime) )\n",
    "plt.hist(uuAngle)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4cc54eba-ca37-4680-9699-37332133afb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "777517.0 10 20201249.0 7775170.0 10.0 aDP, nD, trueStatePop, statePop, /aDP \n",
      "7775170.0 7775170 0.3848856078156355\n"
     ]
    }
   ],
   "source": [
    "print(r3(aDP), nDistricts, trueStatePop, statePop, statePop/aDP, \"aDP, nD, trueStatePop, statePop, /aDP \")\n",
    "HDweight = [0 for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    HDweight[t] = tractPop[t] / trueStatePop #skipping the cutList tracts\n",
    "print(statePop,np.sum([tractPop[t] for t in populatedTractList]), np.sum(HDweight)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dac2348a-060a-40a9-beeb-02ff70ec1aa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUN FOR ALL STATES.  For extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"RUN FOR ALL STATES.  For extended peninsulas, need to broaden the 'neighborCountyList' to include all co-squished counties' neighbors\")\n",
    "opt2nbrCtyList = [neighborCountyList[c].copy() for c in range(nCounties)]\n",
    "for i in range(nClips):\n",
    "    cList = toSquishCountiesList[i]\n",
    "    for c in cList:\n",
    "        for cc in cList:\n",
    "            for ccc in neighborCountyList[cc]:\n",
    "                if ccc not in opt2nbrCtyList[c] and ccc != c:\n",
    "                    opt2nbrCtyList[c].append(ccc)\n",
    "#for c in range(nCounties):\n",
    "#    for cc in opt2nbrCtyList[c]:\n",
    "#        plotPoly(countyGeom[cc])\n",
    "#    plotCenter(c,countyGeom[c])\n",
    "#    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3873aaa2-0c39-4ac0-99c3-92dd7c18c555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\n",
      "For each Home District, we will consider a wedge as one-from-full when it is within 1028.632 of targetWedgePop\n",
      "I am working on HD number 0 of 14191 HDs.  Elapsed sec = 0\n",
      "I am working on HD number 400 of 14191 HDs.  Elapsed sec = 67\n",
      "I am working on HD number 800 of 14191 HDs.  Elapsed sec = 133\n",
      "I am working on HD number 1200 of 14191 HDs.  Elapsed sec = 202\n",
      "I am working on HD number 2075 of 14191 HDs.  Elapsed sec = 264\n",
      "I am working on HD number 2756 of 14191 HDs.  Elapsed sec = 330\n",
      "I am working on HD number 3355 of 14191 HDs.  Elapsed sec = 392\n",
      "I am working on HD number 4441 of 14191 HDs.  Elapsed sec = 464\n",
      "I am working on HD number 4842 of 14191 HDs.  Elapsed sec = 531\n",
      "I am working on HD number 5246 of 14191 HDs.  Elapsed sec = 601\n",
      "I am working on HD number 7336 of 14191 HDs.  Elapsed sec = 671\n",
      "I am working on HD number 8917 of 14191 HDs.  Elapsed sec = 730\n",
      "I am working on HD number 9319 of 14191 HDs.  Elapsed sec = 786\n",
      "I am working on HD number 9721 of 14191 HDs.  Elapsed sec = 847\n",
      "I am working on HD number 10123 of 14191 HDs.  Elapsed sec = 914\n",
      "I am working on HD number 14144 of 14191 HDs.  Elapsed sec = 982\n",
      "990.529 seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\n",
      "vtd avg and sd usage are 0.99992 0.15521\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qwg4AoBDCDgCgEMIOAKAQwg4AoBDCDgCgEMIOAKAQwg4AoBCjh3sCAP/X5JUPDmnc4Xvmn+eZAFxarNgBABTCih3AWRjqqmJiZRG4cKzYAQAUQtgBABRC2AEAFMJ77LgkVPJ+JgC4XFmxAwAohBU7ho1VOAA4t6zYAQAUQtgBABRC2AEAFELYAQAUwsUTAP+Li3qAS5kVOwCAQpxV2G3YsCGTJ09OVVVVGhoasnfv3ucdv23btkydOjVVVVWZPn16du7cOeD7d955Z6ZOnZorrrgiL3/5y9PU1JRvf/vbZzM1AIDLVsVht3Xr1rS0tGTt2rU5cOBAZsyYkebm5jz55JODjt+zZ08WL16cd7/73Xn44YezYMGCLFiwII888kj/mNe+9rVZv359vvvd7+ab3/xmJk+enLe85S05duzY2R8ZAMBlZkRfX19fJTs0NDRk9uzZWb9+fZKkt7c3dXV1Wb58eVauXHna+IULF6anpyc7duzo33bttdemvr4+GzduHPRndHd3p7q6Ov/0T/+UN73pTb90Ts+O7+rqyrhx4yo5HIaR9zLxQh2+Z/45f87z8Xt5PuYJXD4q6ZyKVuxOnjyZ/fv3p6mp6bknGDkyTU1NaW9vH3Sf9vb2AeOTpLm5+YzjT548mU2bNqW6ujozZsyoZHoAAJe1iq6KPX78eE6dOpWampoB22tqavLYY48Nuk9HR8eg4zs6OgZs27FjRxYtWpSf/vSnueqqq7J79+6MHz9+0Oc8ceJETpw40f91d3d3JYcBAFCki+aq2Ouvvz4HDx7Mnj17csMNN+Tmm28+4/v2WltbU11d3f+oq6u7wLMFALj4VBR248ePz6hRo9LZ2Tlge2dnZ2prawfdp7a2dkjjr7jiirzmNa/Jtddem0996lMZPXp0PvWpTw36nKtWrUpXV1f/4+jRo5UcBgBAkSoKuzFjxmTmzJlpa2vr39bb25u2trY0NjYOuk9jY+OA8Umye/fuM47/38/7v0+3/m9jx47NuHHjBjwAAC53FX/yREtLS5YuXZpZs2Zlzpw5WbduXXp6erJs2bIkyZIlSzJp0qS0trYmSVasWJF58+bl3nvvzfz587Nly5bs27cvmzZtSpL09PTkz//8z/O2t70tV111VY4fP54NGzbk8ccfz+///u+fw0MFAChbxWG3cOHCHDt2LGvWrElHR0fq6+uza9eu/gskjhw5kpEjn1sInDt3bjZv3pzbb789q1evzpQpU7J9+/ZMmzYtSTJq1Kg89thjuf/++3P8+PFceeWVmT17dr7xjW/kDW94wzk6TACA8lV8H7uLkfvYXZrcx44Xyn3sgMvBebuPHQAAFy9hBwBQCGEHAFAIYQcAUAhhBwBQCGEHAFAIYQcAUIiKb1AMQGWGem8897sDXigrdgAAhRB2AACFEHYAAIUQdgAAhXDxBHDJGupFCYkLE4DLgxU7AIBCWLEDLguVrO4BXKqs2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFMJHinHO+egmABgeVuwAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAArhI8UACjbUj/g7fM/88zwT4EKwYgcAUAhhBwBQCGEHAFAIYQcAUAhhBwBQCFfFAlwkXMEKvFBW7AAACiHsAAAKIewAAAoh7AAACiHsAAAK4apYhmSoV+sBAMPHih0AQCGEHQBAIYQdAEAhhB0AQCGEHQBAIYQdAEAhhB0AQCGEHQBAIYQdAEAhzirsNmzYkMmTJ6eqqioNDQ3Zu3fv847ftm1bpk6dmqqqqkyfPj07d+7s/97Pf/7zfOQjH8n06dNzxRVXZOLEiVmyZEmeeOKJs5kaAMBlq+Kw27p1a1paWrJ27docOHAgM2bMSHNzc5588slBx+/ZsyeLFy/Ou9/97jz88MNZsGBBFixYkEceeSRJ8tOf/jQHDhzIHXfckQMHDuSBBx7IoUOH8ra3ve2FHRkAwGVmRF9fX18lOzQ0NGT27NlZv359kqS3tzd1dXVZvnx5Vq5cedr4hQsXpqenJzt27Ojfdu2116a+vj4bN24c9Gf867/+a+bMmZMf/ehHufrqq3/pnLq7u1NdXZ2urq6MGzeuksNhiHxWLFw8Dt8zf8hjh/rPbiXPCVxYlXRORSt2J0+ezP79+9PU1PTcE4wcmaamprS3tw+6T3t7+4DxSdLc3HzG8UnS1dWVESNG5GUve1kl0wMAuKyNrmTw8ePHc+rUqdTU1AzYXlNTk8cee2zQfTo6OgYd39HRMej4n/3sZ/nIRz6SxYsXn7FKT5w4kRMnTvR/3d3dXclhAAAU6aK6KvbnP/95br755vT19eUTn/jEGce1tramurq6/1FXV3cBZwkAcHGqKOzGjx+fUaNGpbOzc8D2zs7O1NbWDrpPbW3tkMY/G3U/+tGPsnv37uc9h7xq1ap0dXX1P44ePVrJYQAAFKmiU7FjxozJzJkz09bWlgULFiT5xcUTbW1tufXWWwfdp7GxMW1tbfnABz7Qv2337t1pbGzs//rZqPve976Xr371q7nyyiufdx5jx47N2LFjK5k6QDFczAScSUVhlyQtLS1ZunRpZs2alTlz5mTdunXp6enJsmXLkiRLlizJpEmT0tramiRZsWJF5s2bl3vvvTfz58/Pli1bsm/fvmzatCnJL6Lu937v93LgwIHs2LEjp06d6n//3Ste8YqMGTPmXB0rAEDRKg67hQsX5tixY1mzZk06OjpSX1+fXbt29V8gceTIkYwc+dwZ3rlz52bz5s25/fbbs3r16kyZMiXbt2/PtGnTkiSPP/54vvSlLyVJ6uvrB/ysr371q/mt3/qtszw0AIDLS8X3sbsYuY/d+efUD5TNfezg4nXe7mMHAMDFS9gBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUouIbFANQnqHeq9L97uDiJuwAGFaiEs4dp2IBAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKMXq4J8DwmrzyweGeAlAof1/gwrNiBwBQCGEHAFAIYQcAUAhhBwBQCBdPADBkLoiAi5sVOwCAQgg7AIBCCDsAgEIIOwCAQgg7AIBCuCoWgEtCJVfkHr5n/nmcCVy8rNgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFGL0cE8AAC52k1c+OOSxh++Zfx5nAs/Pih0AQCHOKuw2bNiQyZMnp6qqKg0NDdm7d+/zjt+2bVumTp2aqqqqTJ8+PTt37hzw/QceeCBvectbcuWVV2bEiBE5ePDg2UwLAOCyVvGp2K1bt6alpSUbN25MQ0ND1q1bl+bm5hw6dCgTJkw4bfyePXuyePHitLa25sYbb8zmzZuzYMGCHDhwINOmTUuS9PT05LrrrsvNN9+c9773vS/8qABgCCo5xQqXghF9fX19lezQ0NCQ2bNnZ/369UmS3t7e1NXVZfny5Vm5cuVp4xcuXJienp7s2LGjf9u1116b+vr6bNy4ccDYw4cP55WvfGUefvjh1NfXD3lO3d3dqa6uTldXV8aNG1fJ4Vz2/FEDSjTU97mdj7+B3mPHuVZJ51R0KvbkyZPZv39/mpqannuCkSPT1NSU9vb2Qfdpb28fMD5Jmpubzzh+KE6cOJHu7u4BDwCAy11FYXf8+PGcOnUqNTU1A7bX1NSko6Nj0H06OjoqGj8Ura2tqa6u7n/U1dWd9XMBAJTikrwqdtWqVenq6up/HD16dLinBAAw7Cq6eGL8+PEZNWpUOjs7B2zv7OxMbW3toPvU1tZWNH4oxo4dm7Fjx571/gAAJapoxW7MmDGZOXNm2tra+rf19vamra0tjY2Ng+7T2Ng4YHyS7N69+4zjAQA4OxXf7qSlpSVLly7NrFmzMmfOnKxbty49PT1ZtmxZkmTJkiWZNGlSWltbkyQrVqzIvHnzcu+992b+/PnZsmVL9u3bl02bNvU/509+8pMcOXIkTzzxRJLk0KFDSX6x2vdCVvYAAC4nFYfdwoULc+zYsaxZsyYdHR2pr6/Prl27+i+QOHLkSEaOfG4hcO7cudm8eXNuv/32rF69OlOmTMn27dv772GXJF/60pf6wzBJFi1alCRZu3Zt7rzzzrM9NgCAy0rF97G7GLmP3dlzHzugRO5jR0nO233sAAC4eAk7AIBCCDsAgEIIOwCAQgg7AIBCCDsAgEIIOwCAQgg7AIBCCDsAgEIIOwCAQgg7AIBCCDsAgEIIOwCAQgg7AIBCCDsAgEIIOwCAQgg7AIBCjB7uCQDAuTZ55YPDPQUYFsKuUP6oAZRjqH/TD98z/zzPhIudU7EAAIUQdgAAhXAqFgDOIadNGU5W7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoh7AAACiHsAAAKIewAAAoxergnwNAN9YOlAbj4+ZvO+WDFDgCgEMIOAKAQwg4AoBDCDgCgEMIOAKAQwg4AoBDCDgCgEO5jdxFwLyMA4FywYgcAUAgrdgBQiKGeATp8z/zzPBOGixU7AIBCCDsAgEIIOwCAQgg7AIBCuHgCAHjBXLhxcbBiBwBQCGEHAFAIp2LPI58oAcDFqJJ/Pzl1emmxYgcAUAgrdgDARckFGZWzYgcAUAhhBwBQCKdiAYAzOtcXArqw8Pw6q7DbsGFD/uqv/iodHR2ZMWNGPv7xj2fOnDlnHL9t27bccccdOXz4cKZMmZKPfvSjeetb39r//b6+vqxduzaf/OQn89RTT+U3fuM38olPfCJTpkw5m+kBAJeR8xGLl+r79ioOu61bt6alpSUbN25MQ0ND1q1bl+bm5hw6dCgTJkw4bfyePXuyePHitLa25sYbb8zmzZuzYMGCHDhwINOmTUuS/OVf/mX+5m/+Jvfff39e+cpX5o477khzc3P+7d/+LVVVVS/8KM8h/08DAMp3qV64MaKvr6+vkh0aGhoye/bsrF+/PknS29uburq6LF++PCtXrjxt/MKFC9PT05MdO3b0b7v22mtTX1+fjRs3pq+vLxMnTsyHPvSh/Mmf/EmSpKurKzU1NfnsZz+bRYsW/dI5dXd3p7q6Ol1dXRk3blwlh1MxYQcAPOtChF0lnVPRit3Jkyezf//+rFq1qn/byJEj09TUlPb29kH3aW9vT0tLy4Btzc3N2b59e5Lkhz/8YTo6OtLU1NT//erq6jQ0NKS9vX3QsDtx4kROnDjR/3VXV1eSXxz4+dZ74qfn/WcAAJeGC9Eez/6MoazFVRR2x48fz6lTp1JTUzNge01NTR577LFB9+no6Bh0fEdHR//3n912pjH/V2tra+66667TttfV1Q3tQAAAzoHqdRfuZz399NOprq5+3jGX5FWxq1atGrAK2Nvbm5/85Ce58sorM2LEiGGcGZXo7u5OXV1djh49et5PoXNheE3L4zUtk9f10tLX15enn346EydO/KVjKwq78ePHZ9SoUens7BywvbOzM7W1tYPuU1tb+7zjn/3Pzs7OXHXVVQPG1NfXD/qcY8eOzdixYwdse9nLXlbJoXARGTdunD8shfGalsdrWiav66Xjl63UPauiGxSPGTMmM2fOTFtbW/+23t7etLW1pbGxcdB9GhsbB4xPkt27d/ePf+UrX5na2toBY7q7u/Ptb3/7jM8JAMDpKj4V29LSkqVLl2bWrFmZM2dO1q1bl56enixbtixJsmTJkkyaNCmtra1JkhUrVmTevHm59957M3/+/GzZsiX79u3Lpk2bkiQjRozIBz7wgfzZn/1ZpkyZ0n+7k4kTJ2bBggXn7kgBAApXcdgtXLgwx44dy5o1a9LR0ZH6+vrs2rWr/+KHI0eOZOTI5xYC586dm82bN+f222/P6tWrM2XKlGzfvr3/HnZJ8uEPfzg9PT35oz/6ozz11FO57rrrsmvXrovuHnacW2PHjs3atWtPO63OpctrWh6vaZm8ruWq+D52AABcnCp6jx0AABcvYQcAUAhhBwBQCGEHAFAIYcd5tWHDhkyePDlVVVVpaGjI3r17n3f8unXrcs011+TFL35x6urq8sEPfjA/+9nPLtBs+WW+/vWv56abbsrEiRMzYsSI/s98fj4PPfRQ3vjGN2bs2LF5zWtek89+9rPnfZ4MXaWv6QMPPJA3v/nN+ZVf+ZWMGzcujY2N+fKXv3xhJsuQnM0/p8/6l3/5l4wePfqMHxDAxU/Ycd5s3bo1LS0tWbt2bQ4cOJAZM2akubk5Tz755KDjN2/enJUrV2bt2rV59NFH86lPfSpbt27N6tWrL/DMOZOenp7MmDEjGzZsGNL4H/7wh5k/f36uv/76HDx4MB/4wAfynve8RwhcRCp9Tb/+9a/nzW9+c3bu3Jn9+/fn+uuvz0033ZSHH374PM+Uoar0NX3WU089lSVLluRNb3rTeZoZF4LbnXDeNDQ0ZPbs2Vm/fn2SX3xKSV1dXZYvX56VK1eeNv7WW2/No48+OuBTSD70oQ/l29/+dr75zW9esHkzNCNGjMgXv/jF572R+Ec+8pE8+OCDeeSRR/q3LVq0KE899VR27dp1AWZJJYbymg7mDW94QxYuXJg1a9acn4lx1ip5TRctWpQpU6Zk1KhR2b59ew4ePHje58e5Z8WO8+LkyZPZv39/mpqa+reNHDkyTU1NaW9vH3SfuXPnZv/+/f2na3/wgx9k586deetb33pB5sy5197ePuB3IEmam5vP+DvApae3tzdPP/10XvGKVwz3VHgBPvOZz+QHP/hB1q5dO9xT4QWq+JMnYCiOHz+eU6dO9X8iybNqamry2GOPDbrPO9/5zhw/fjzXXXdd+vr68j//8z+55ZZbnIq9hHV0dAz6O9Dd3Z3//u//zotf/OJhmhnnyl//9V/nmWeeyc033zzcU+Esfe9738vKlSvzjW98I6NHy4JLnRU7LhoPPfRQ/uIv/iJ/+7d/mwMHDuSBBx7Igw8+mLvvvnu4pwYMYvPmzbnrrrvyhS98IRMmTBju6XAWTp06lXe+852566678trXvna4p8M5IM05L8aPH59Ro0als7NzwPbOzs7U1tYOus8dd9yRd73rXXnPe96TJJk+fXr/Zwj/6Z/+6YDPIObSUFtbO+jvwLhx46zWXeK2bNmS97znPdm2bdtpp9u5dDz99NPZt29fHn744dx6661JfnF6va+vL6NHj85XvvKV/PZv//Ywz5JK+Dcl58WYMWMyc+bMARdC9Pb2pq2tLY2NjYPu89Of/vS0eBs1alSSxDU+l6bGxsYBvwNJsnv37jP+DnBp+PznP59ly5bl85//fObPnz/c0+EFGDduXL773e/m4MGD/Y9bbrkl11xzTQ4ePJiGhobhniIVsmLHedPS0pKlS5dm1qxZmTNnTtatW5eenp4sW7YsSbJkyZJMmjQpra2tSZKbbropH/vYx/Lrv/7raWhoyPe///3ccccduemmm/oDj+H1zDPP5Pvf/37/1z/84Q9z8ODBvOIVr8jVV1+dVatW5fHHH8/nPve5JMktt9yS9evX58Mf/nD+8A//MP/8z/+cL3zhC3nwwQeH6xD4Pyp9TTdv3pylS5fmvvvuS0NDQzo6OpIkL37xi1NdXT0sx8BAlbymI0eOzLRp0wbsP2HChFRVVZ22nUtEH5xHH//4x/uuvvrqvjFjxvTNmTOn71vf+lb/9+bNm9e3dOnS/q9//vOf99155519r371q/uqqqr66urq+v74j/+477/+678u/MQZ1Fe/+tW+JKc9nn0dly5d2jdv3rzT9qmvr+8bM2ZM36te9aq+z3zmMxd83pxZpa/pvHnznnc8w+9s/jn939auXds3Y8aMCzJXzj33sQMAKIT32AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABRC2AEAFELYAQAUQtgBABTi/wNzbp93ww8VRAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing option 2 .......\n",
    "# BELOW modified Dec23 to use inter-unit distances and angles, (counties still wedgeIntersxn) otherwise similar to HD2\n",
    "# --THIS ESSENTIALLY TAKES THE ORIGINAL TRACT-BASED HD centerpoints, and redraws HD2 districts after convexifying corners and convex_hull\n",
    "print(\"Here is the main code to draw 4-wedge Home Districts for each populated HD centerpoint\") \n",
    "#this is a hybrid of HD2 and the organic code, in that all wedge tracts must be in a chain of neighboring counties\n",
    "#General sequence: draw 4 equi-angle wedges with random starting orientation.  If not all can fill a quarter aDP ...\n",
    "# ... find the closest map boundary point to the tract center point in the shortest wedge.  Orient the 0th wedge to face this point\n",
    "# ... determine the \"maxWedgePop\" for this short wedge and the other three wedges extended past the boundary\n",
    "# ...  if this results in only one short wedge, or opposing short wedges, adjust wedge angles\n",
    "# Regardless of whether we re-oriented or adjusted angles, compute each wedgePop for infinite radius\n",
    "#   ... then adjust other targetWedgePops if some found to be short.\n",
    "#   ... for non-shorted wedges, use bisection method to adjust radius to meet targetWedgePop\n",
    "#   ... once total HDpop within tolerance, add/subtract a tract fraction to fulfill HDpop\n",
    "#  create a list of fully used tracts per HD, save the tract# of the split tract and its fractional use\n",
    "#  Compute tractUse across all HDs at the end from the tractUsageLists.  Defer patching and partisan calcs to another code\n",
    "\n",
    "pi=3.1415926536\n",
    "#MAPhull = MAP.convex_hull #used for exitAngle calc.  Defined in an above block to allow user modification\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2.*pi / nWedges\n",
    "angle, angle2, wedgePoly = [0.]*nWedges, [0.]*nWedges, [dummyPoly]*nWedges  #start and stop angle of each wedge\n",
    "pt1, pt2, pt3 = [Point(0,0)]*nWedges, [Point(0,0)]*nWedges, [Point(0,0)]*nWedges #these four define the polygon wedge for a growing home district\n",
    "tractUse = [0.] * nHDs  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "HDtractList = [list()]*nHDs\n",
    "splitTractNo = [-999]*nHDs  #the tract that is only partially used to finish each HD\n",
    "splitTractUse = [0.]*nHDs  #and this is what fraction of the split tract is included\n",
    "HDvPop = [0.]*nHDs\n",
    "#HDweight = [tractPop[t] / (statePop - excludedPop) for t in range(nTracts)]  #relative weight of each drawn HD\n",
    "HDPoly1 = [dummyPoly]*nHDs  #final 4-wedge shape, unionized from blockgroups / tracts\n",
    "HDarea = [0.]*nHDs  #geographical area of each home district (after boundary trim)\n",
    "HDradius = [[0.]*nWedges for t in range(nHDs)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nHDs)] #final starting angle for each HD wedge\n",
    "angle0 = [-999.] * nHDs  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "avgTractPop = statePop/len(populatedTractList)\n",
    "tolerPops = [0.8 * avgTractPop, 0.5 * np.max(tractPop)]  #threshold gap before adding one more unit to a wedge \n",
    "tolerPop = tolerPops[0]\n",
    "print(\"For each Home District, we will consider a wedge as one-from-full when it is within\",r3(tolerPops[0]),\"of targetWedgePop\")\n",
    "tractPrintInterval = 400  #for tracking progress\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #HD2\n",
    "maxAngle = 1.8*pi/2. # limit to avoid wedges too close to half a pie\n",
    "minAdjRatio = 0.5  #min ratio of pop of adjacent wedge (to min wedge) to targetWedgePop to not consider this a corner HD\n",
    "maxUncap = 0.5 #the max ratio of uncaptured pop in a maxWedge vs. target wedge pop that we will not stretch to include (7/23)\n",
    "oppWt = 0.0    #the fraction of gap between normal targetWedgePop and the minWedgePop that we allow the opposite wedge to add to tgtPop = minWedgePop\n",
    "maxWedgePop = [0.] * nWedges  #wedge pop if we explode wedge to maximum radius\n",
    "printDebug = \"no\"\n",
    "codeStartTime = time.time()\n",
    "hdCPpop = [0. for t in range(nHDs)]\n",
    "for t in populatedTractList:\n",
    "    hdCPpop[t] = tractPop[t]\n",
    "#hdCPpop = [HDweight[t] * statePop for t in range(nHDs)]  #may not work for states w/cut districts\n",
    "countyHDlist = [list() for c in range(nCounties)]\n",
    "for t in populatedTractList:\n",
    "    countyHDlist[HDcountyNo[t]].append(t)\n",
    "\n",
    "for kkkj, t in enumerate(populatedTractList):  #range(nTracts) : #loop on each tract. \n",
    "    hC = HDcountyNo[t]     #this tract's home county\n",
    "    HDtractList[t] = [t] #seed the list of tracts in this HD\n",
    "    wedgeList = [list() for w in range(nWedges)]   #list of each wedge's tracts, excluding home tract\n",
    "    barredList = list()  #list of wedge numbers for which we are barred from altering their targetWedgePops\n",
    "    if (kkkj % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on HD number {0} of {1} HDs.  Elapsed sec = {2}\".format(t,nHDs,int(time.time()-codeStartTime) ) )  \n",
    "    nActiveWedges = nWedges #active wedges have not run over boundary\n",
    "    wedgePop = [0.]*nWedges\n",
    "    targetWedgePop = [aDP / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    \n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to the midangle of our starting wedge\n",
    "    wedgeAngle = [avgWedgeAngle for w in range(nWedges) ] #reset to equi-angle when starting each tract\n",
    "    for nW in range(nWedges-1):\n",
    "        wedgeAngle[nW] += random.uniform(-0.0001,0.0001)  #this wiggle solves a later indexing ambiguity for corner tracts\n",
    "    wedgeAngle[nWedges-1] = 2.*pi - (np.sum(wedgeAngle) - wedgeAngle[nWedges-1] ) #squaring up to 2pi total\n",
    "    startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "    for nW in range(1,nWedges):\n",
    "        startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "    endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "    #  TRIAL LOOP TO SEE WHICH WEDGES CAN FILL TO TARGET POP w/equiangle wedges\n",
    "    maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]    \n",
    "    maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "    willFill = [0] * nWedges #would this wedge meet its target with infinite radius?\n",
    "    for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "        iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "        #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList) \n",
    "        nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "        maxWedgePop[nW] += (iCP + nonCP)\n",
    "        wedgeList[nW] = Li + Ln\n",
    "        if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "            willFill[nW] = 1\n",
    "     \n",
    "    if np.sum(willFill) < nWedges:  #at least one wedge couldn't reach its wedgePop target (went over boundary) ...\n",
    "        minW = maxWedgePop.index(np.min(maxWedgePop)) #.. so we will orient the 0th wedge to face the shortest wedgePop's closest boundary\n",
    "        exitAngle = getExitAngle(hdCP[t],maxWedgePoly[minW], MAPhull, startAngle[minW], endAngle[minW])\n",
    "        angle0[t] = exitAngle - 0.5*(2.*pi / nWedges)  #Now reset all angles based on new starting orientation.  All wedge angles still equal\n",
    "        startAngle = [angle0[t] for nW in range(nWedges)]\n",
    "        for nW in range(1,nWedges):\n",
    "            startAngle[nW] = startAngle[nW-1] + wedgeAngle[nW-1]\n",
    "        endAngle = [startAngle[nW] + wedgeAngle[nW] for nW in range(nWedges)]\n",
    "        maxWedgePoly = [buildWedge(hdCP[t],startAngle[nW],endAngle[nW], maxD, xScale) for nW in range(nWedges) ]\n",
    "        \n",
    "        willFill = [0]*nWedges\n",
    "        #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation (wedge angles still constant)\n",
    "        maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop        \n",
    "        for nW in range(nWedges):   #... then add in-county and nonCounty wedge Pop from contiguous chain of counties\n",
    "            iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])\n",
    "            #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "            nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                    opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "            maxWedgePop[nW] += (iCP + nonCP)\n",
    "            wedgeList[nW] = Li + Ln\n",
    "            if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                willFill[nW] = 1       \n",
    "    \n",
    "    if np.sum(willFill) < nWedges:  #check if we need to modify wedge angles after possible re-orientation.  \n",
    "        # If so, re-draw maxWedgePoly's, re-calc maxWedgePops\n",
    "        nUnfilledWedges = nWedges - np.sum(willFill)\n",
    "        isChange, newInclA = getNewAngles(maxWedgePop, targetWedgePop, aDP, levelL, minAdjRatio, maxAngle, maxAngleRatio, nUnfilledWedges )\n",
    "        if isChange: #no longer equi-angle\n",
    "            newStartAngle = [0.5*(startAngle[nW]+endAngle[nW]) - 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            newEndAngle =   [0.5*(startAngle[nW]+endAngle[nW]) + 0.5*newInclA[nW] for nW in range(nWedges) ]\n",
    "            startAngle, endAngle, wedgeAngle = newStartAngle.copy(), newEndAngle.copy(), newInclA.copy()\n",
    "            maxWedgePoly = [ buildWedge(hdCP[t],startAngle[nW],endAngle[nW], \n",
    "                                        maxD/math.cos(0.5*wedgeAngle[nW]), xScale ) for nW in range(nWedges) ]\n",
    "            willFill = [0]*nWedges\n",
    "            #Now re-calc each maxWedgePop if we extend wedge's radius past map with new angle0 orientation and new wedge angles\n",
    "            maxWedgePop =[ hdCPpop[t]*wedgeAngle[nW]/(2.*pi) for nW in range(nWedges) ]  #seed wedge with fraction of its home tract pop\n",
    "            for nW in range(nWedges):   #... then add in-county Pop and wedge Pop from contiguous chain of counties\n",
    "                iCP, Li   = getCountyWP(t,maxWedgePoly[nW], hdCP, hdCPpop, countyHDlist[hC])                \n",
    "                #nonCP, Ln = getNonCWP(maxWedgePoly[nW], hC, tractCP, tractPop, countyGeom, countyPop, countyTractList, neighborCountyList)\n",
    "                nonCP, Ln = getNonCWP_c(startAngle[nW],endAngle[nW], hC, hdCP, hdCPpop, countyGeom, countyPop, countyHDlist,\n",
    "                                        opt2nbrCtyList, uuDist[t], uuAngle[t], maxWedgePoly[nW])\n",
    "                maxWedgePop[nW] += (iCP + nonCP)\n",
    "                wedgeList[nW] = Li + Ln\n",
    "            minW = wedgeAngle.index(np.max(wedgeAngle)) #should be 0th wedge, but playing it safe.  This is the 1st wide-angle wedge ..\n",
    "            oppW = int(int(minW+nWedges/2)%nWedges)   #and its opposite\n",
    "            if maxWedgePop[minW] > maxWedgePop[oppW] and maxWedgePop[oppW] < aDP / nWedges:  #minW and oppW are flipped after inclA adjmt\n",
    "                oppW = minW\n",
    "                minW = int(int(minW+nWedges/2)%nWedges)\n",
    "            if maxWedgePop[minW] < targetWedgePop[oppW]:  #we need to constrain oppW in this HD2 implementation; redistribute tgt to adjacents\n",
    "                maxNonOppW = np.sum(maxWedgePop) - maxWedgePop[oppW] #...but only if other wedges can pick up slack; need to check\n",
    "                minOppWedgePop = aDP - maxNonOppW  #cannot set oppW target below this or we won't reach aDP across all wedges\n",
    "                barredList = [oppW]\n",
    "                targetWedgePop[oppW] = max(minOppWedgePop, maxWedgePop[minW] + oppWt * (targetWedgePop[oppW] - maxWedgePop[minW]) )\n",
    "                tWPgap = aDP / float(nWedges) - targetWedgePop[oppW] \n",
    "                for nW in range(nWedges):\n",
    "                    if nW != minW and nW != oppW:\n",
    "                        targetWedgePop[nW] += tWPgap/float(nWedges - 2.)  #if oppW target is limited, allot to adjacent wedges\n",
    "            for nW in range(nWedges):\n",
    "                if maxWedgePop[nW] > targetWedgePop[nW]:\n",
    "                    willFill[nW] = 1  \n",
    "    \n",
    "    if abs(np.sum(targetWedgePop) / aDP - 1.) > 0.001:\n",
    "        raise Exception(\"FAIL! Our target wedgepops\",targetWedgePop, np.sum(targetWedgePop),\"no longer add up to\",aDP)\n",
    "    \n",
    "    if np.sum(willFill) == nWedges:  #all wedges will fill.  Will any leave a small near-boundary remnant?  If so, pick up smallest remnant\n",
    "        remnantWedgePopFrac = [ ( maxWedgePop[w] - targetWedgePop[w] )/targetWedgePop[w] for w in range(nWedges) ]\n",
    "        if np.min(remnantWedgePopFrac) < maxUncap :  #lessen tWP's of *adjacent* wedges, then increase this wedge's target --> max\n",
    "            minW = remnantWedgePopFrac.index(np.min(remnantWedgePopFrac))\n",
    "            oppW = int((minW+nWedges/2)%nWedges)\n",
    "            #print(\"filling to boundary for tract,wedge, tWP, mWP =\",t,minW, r3(targetWedgePop[minW]), maxWedgePop[minW])\n",
    "            for nW in range(nWedges):\n",
    "                if nW != minW and nW != oppW:\n",
    "                    targetWedgePop[nW] -= (remnantWedgePopFrac[minW] * targetWedgePop[minW] ) / (nWedges - 2.)\n",
    "            targetWedgePop[minW] = maxWedgePop[minW]\n",
    "            #       OK, READY TO SOLVE FOR NON-FILLED WEDGE SIZES once we adjust targets for unfilled wedges\n",
    "    #print(t,\" prior to rebalanceTWP call, mWPs, tWPs are\",maxWedgePop, targetWedgePop)\n",
    "    targetWedgePop, willFill = rebalanceTWPs(maxWedgePop, targetWedgePop, barredList)\n",
    "    #if np.max(targetWedgePop) - np.min(targetWedgePop) > 0.02 * aDP: #debug\n",
    "    #    print(\"for tract\",t,\",  After rebalancing but before tweaks for blocky pop adds: willFill, targetWedgePops and maxWedgePops are ...\")\n",
    "    #    for w in range(nWedges):\n",
    "    #        print(w, willFill[w],r3(targetWedgePop[w]),int(maxWedgePop[w]) )\n",
    "    for w in range(nWedges):  #WHEW!  WE CAN FINALLY ASSIGN ALL WEDGES' POPS AND TRACTLISTS\n",
    "        loop = 0\n",
    "        if willFill[w] == 0:  #wedge is maxed out\n",
    "            wedgePop[w] = maxWedgePop[w]\n",
    "            #wedgeList[w] = ...  #we already captured this list = maxWedge list\n",
    "            HDradius[t][w] = maxD/math.cos(0.5*wedgeAngle[w])\n",
    "        else:  #need to solve for wedge radius to meet targetPop.  Use bisection as scipy minimize no faster\n",
    "            HDcpWP = hdCPpop[t] * wedgeAngle[w]/(2.*pi)\n",
    "            nonHDcpTWP = targetWedgePop[w] - HDcpWP\n",
    "            #wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeB(nontractTWP,t,hC,maxWedgePoly[w],tolerPop,tractCP, tractPop,\n",
    "            #                                                        countyNo,countyTractList,countyGeom, neighborCountyList,uuDist[t])\n",
    "            wedgePop[w], wedgeList[w], HDradius[t][w] = solveWedgeC(nonHDcpTWP,t,hC, maxWedgePoly[w],startAngle[w], endAngle[w], tolerPop,\n",
    "                                                                    hdCP, hdCPpop,HDcountyNo, countyHDlist,countyGeom,\n",
    "                                                                    opt2nbrCtyList,uuDist[t],uuAngle[t])\n",
    "            popGap = nonHDcpTWP - wedgePop[w]  #gap between target and solved wedge pop; can be negative\n",
    "            notYetAdjusted, nextW = True, w+1  #looking to adjust a later wedge's target pop to minimize drift in total HD pop\n",
    "            while notYetAdjusted and nextW < nWedges:\n",
    "                if willFill[nextW] == 1 and maxWedgePop[nextW] > targetWedgePop[nextW] + popGap :  #can accommodate\n",
    "                    targetWedgePop[nextW] += popGap\n",
    "                    notYetAdjusted = False\n",
    "                nextW +=1          \n",
    "\n",
    "        HDangle[t][w] = startAngle[w]\n",
    "    #print(t,\"tract. After TWP adjustment, targetWedgePops are\",targetWedgePop)\n",
    "    HDtractList[t] = [t]\n",
    "    totPop = hdCPpop[t]\n",
    "    for nW in range(nWedges):\n",
    "        for tt in wedgeList[nW]:\n",
    "            HDtractList[t].append(tt)  #building the list of tracts in the Home District  (we'll keep up with below changes)\n",
    "            totPop += hdCPpop[tt]\n",
    "    offsetPop = totPop - aDP  #we have solved for all wedge radii to get each within one tractPop of target ... \n",
    "    HDvPop[t] = totPop\n",
    "    #if offsetPop > 0:   #... now include a splitPoly to nail the avg District Pop\n",
    "    #    isOver = True  #we slightly overshot.  ID the farthest-away tract for split-jettison\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], hdCPpop, neighborList)\n",
    "    #    HDvPop[t] = totPop - (1. - splitTractUse[t]) * tractPop[splitTractNo[t]]\n",
    "    #if offsetPop < 0:\n",
    "    #    isOver = False  #we have undershot.  Pick up the nearest contiguous tract that can be split to fill the gap\n",
    "    #    splitTractNo[t], splitTractUse[t] = getPartialTract(t, HDtractList[t], offsetPop, uuDist[t], tractPop, neighborList)\n",
    "    #    HDtractList[t].append(splitTractNo[t])\n",
    "    #    HDvPop[t] = totPop + splitTractUse[t]*tractPop[splitTractNo[t]]       \n",
    "    #if abs(1. - HDvPop[t]/aDP) > 0.005 :  #something went wrong with pop assignation for this HD\n",
    "    #    print(\"WARNING! Total Home District pop was\",HDvPop[t],\"vs target\",aDP,\"for tract\",t)   ##### hdCP topology not yet known; skip partial\n",
    "        \n",
    "    HDPoly1[t] = dummyPoly  #tractGeom[t] #skip constructing the HD poly shape.  Do compute area of the Home District, including final partial\n",
    "    HDarea[t] = 0.\n",
    "    for tt in HDtractList[t]:\n",
    "        if tt == splitTractNo[t]:\n",
    "            tractUse[tt] += nDistricts * HDweight[t] * splitTractUse[t] \n",
    "            #HDarea[t]   += tractArea[tt] * splitTractUse[t]  #these are original (nonsquished) areas\n",
    "            \n",
    "        else:\n",
    "            tractUse[tt] += nDistricts * HDweight[t]\n",
    "            #HDarea[t]    += tractArea[tt]\n",
    "            #HDPoly1[t] = HDPoly1[t].union(tractGeom[tt])\n",
    "    HDPoly1[t] = buildPoly(hdCP[t],HDradius[t], HDangle[t] )\n",
    "    #HDarea[t] = HDPoly1[t].area + splitTractUse[t] * tractArea[splitTractNo[t]]  \n",
    "    #if splitTractUse[t] > 0.5:\n",
    "    #    HDPoly1[t] = HDPoly1[t].union(tractGeom[splitTractNo[t]])\n",
    "                          \n",
    "    #ALL DONE ESTABLISHING THE FINAL HDTRACTLIST.  FINALIZE STATS\n",
    "totalTime = time.time() - codeStartTime\n",
    "print(r3(totalTime),\"seconds elapsed. All done computing HD shapes and tractlists.  Here is a histogram of vtd usage.\")\n",
    "n_bins=50\n",
    "popTractUse, plotWts = [tractUse[t] for t in populatedTractList], [HDweight[t] for t in populatedTractList]\n",
    "\n",
    "origHDuseAvg, origHDuseSD = getWeightedAvgAndSD(tractUse,HDweight)\n",
    "print(\"vtd avg and sd usage are\",r5(origHDuseAvg), r5(origHDuseSD) )\n",
    "\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "ax.hist(popTractUse, bins=n_bins, weights=plotWts)\n",
    "plt.show()\n",
    "HDvPop = [np.sum([hdCPpop[tt] for tt in HDtractList[t]]) for t in range(nHDs)]\n",
    "plt.scatter([hdCPpop[t] for t in populatedTractList],[HDvPop[t] for t in populatedTractList] )\n",
    "print(\"here are your HD pops\")\n",
    "plt.show()\n",
    "origHDHDtractList = [HDtractList[t].copy() for t in range(nHDs)] #save for posterity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7137f13d-05fe-4e85-a53a-43132e83fe75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NYupper\n"
     ]
    }
   ],
   "source": [
    "print(STATE)\n",
    "date = \"27Apr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "37c238bb-b340-431b-ad11-95e4409d9699",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will write the polygon-based results to a file\n"
     ]
    }
   ],
   "source": [
    "#last block of option 2 (i.e. generating polygonal HDs from scratch)\n",
    "print(\"Now we will write the polygon-based results to a file\")\n",
    "tractNo = [t for t in range(nHDs) ]\n",
    "HDangle0 = [HDangle[t][0] for t in range(nHDs) ]\n",
    "HDangle1 = [HDangle[t][1] for t in range(nHDs) ]\n",
    "HDangle2 = [HDangle[t][2] for t in range(nHDs) ]\n",
    "HDangle3 = [HDangle[t][3] for t in range(nHDs) ]\n",
    "HDradius0 = [HDradius[t][0] for t in range(nHDs)]\n",
    "HDradius1 = [HDradius[t][1] for t in range(nHDs)]\n",
    "HDradius2 = [HDradius[t][2] for t in range(nHDs)]\n",
    "HDradius3 = [HDradius[t][3] for t in range(nHDs)]\n",
    "hdCPx, hdCPy =   [hdCP[t].x for t in range(nHDs)], [hdCP[t].y for t in range(nHDs)]\n",
    "\n",
    "paramList = [\"STATE\",\"statePop\",\"nDistricts\",\"nHDs\",\"nWedges\",\"popn-toler\",\"levelL\", \"maxAngleRatio\",\n",
    "             \"maxAngle\",\"minAdjRatio\",\"maxUncap\", \"oppWt\", \"calc sec\",\"xScale\",\"outputs...\"]\n",
    "paramValues = [STATE,statePop, nDistricts, nHDs, nWedges, tolerPop, levelL, maxAngleRatio, maxAngle, minAdjRatio, maxUncap,\n",
    "               oppWt, totalTime, xScale, -777]\n",
    "for i in range(nHDs-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "HDdf = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"countyNo\":HDcountyNo,\n",
    "                    \"tractNo\":tractNo,\"centroid x\":hdCPx,\"centroid y\":hdCPy,\"tractPop\":hdCPpop,\n",
    "                    \"HDweight\":HDweight,\"HD-pop\":HDvPop,\"HDarea\":HDarea, \"countyNo\":countyNo,\n",
    "                    \"startAngle\":angle0,\"HDangle0\":HDangle0,\"HDangle1\":HDangle1,\"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\n",
    "                    \"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\"HDradius2\":HDradius2, \"HDradius3\":HDradius3,\"tractUse\":tractUse,\n",
    "                    \"splitTractNo\":splitTractNo,\"splitTractUse\":splitTractUse,\"HDtractList\":HDtractList} ) \n",
    "\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Bnopatch\"+str(int(nDistricts))+\"nW4.csv\"  #solveWedgeB\n",
    "outname = STATE+str(int(nHDs))+\"convexHD2_\"+str(int(nDistricts))+\"nW4_\"+date+\".csv\"  #solveWedgeC\n",
    "#outname = STATE+str(int(nTracts))+\"HD2Buutpatch\"+str(int(nDistricts))+\"nW4.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "HDdf.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "075013a3-f3ee-4a44-ac4a-27a7492e027a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f4883ddf-96f0-46a9-9d58-5c9664e90b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7775170.0 20201249.0 2992551.0313198953 20201249\n"
     ]
    }
   ],
   "source": [
    "print(statePop,trueStatePop,np.sum(hdCPpop),np.sum(tractPop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6e7e8ccd-8e11-4d6d-970d-b059401e0a3b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the original HD polygon shape assignment csv, e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv ./2024state_HD_output/NYupper14191convexHD2_10nW4_27Apr.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read back in the original HD shapes\n",
      "Ready to capture unit centroids based on these HD poly shapes; see next block\n"
     ]
    }
   ],
   "source": [
    "#OPTION 2 --> 1 - we already have an ensemble of HDpoly's and their weights (from running option 2 above)\n",
    "#read them in\n",
    "#determine each cluster's required area fraction capture to get 1.00 cluster use across the HD set\n",
    "#then determine total pop (including cluster in/out) for each HD, and total unit use\n",
    "#then move into triage\n",
    "infilename = input(\"enter the original HD polygon shape assignment csv, \"+ \n",
    "                   \"e.g. ./2024state_HD_output/FL7211convexHD2_26nW4_03Mar.csv or ./state_HD_output/AZ9HD1tol0.005nW425Mar.csv\")\n",
    "shapeDF = pd.read_csv(infilename)\n",
    "HDvPop = shapeDF[\"HD-pop\"].to_list()\n",
    "HDweight = shapeDF['HDweight'].to_list() #shapeDF['HDwt'].to_list() or #shapeDF['HDweight'].to_list()\n",
    "HDarea = shapeDF['HDarea'].to_list()\n",
    "#tractPop = shapeDF['tractPop'].to_list()   #we will use vtd-mapped pops instead\n",
    "nHDs = len(shapeDF)\n",
    "hdCPx = shapeDF['centroid x'].to_list()   #note: these may be translated from outcroppings into a convex representation of the map\n",
    "hdCPy = shapeDF['centroid y'].to_list()\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs) ]\n",
    "HDradius0 = shapeDF['HDradius0'].to_list()\n",
    "HDradius1 = shapeDF['HDradius1'].to_list()\n",
    "HDradius2 = shapeDF['HDradius2'].to_list()\n",
    "HDradius3 = shapeDF['HDradius3'].to_list()\n",
    "HDangle0 = shapeDF['HDangle0'].to_list()\n",
    "HDangle1 = shapeDF['HDangle1'].to_list()\n",
    "HDangle2 = shapeDF['HDangle2'].to_list()\n",
    "HDangle3 = shapeDF['HDangle3'].to_list()\n",
    "HDradius, HDangle = list(), list()\n",
    "for t in range(nHDs):\n",
    "    HDradius.append( [HDradius0[t],HDradius1[t],HDradius2[t],HDradius3[t] ] )\n",
    "    HDangle.append(  [HDangle0[t], HDangle1[t], HDangle2[t], HDangle3[t]  ] )\n",
    "print(\"I have read back in the original HD shapes\")\n",
    "popHDlist = list()\n",
    "HDpoly = [dummyPoly for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    if HDweight[t] > 0.000001:\n",
    "        popHDlist.append(t)\n",
    "        HDpoly[t] = buildArcPoly(hdCP[t],HDradius[t], HDangle[t], xScale)\n",
    "if \"countyNo\" in shapeDF.columns.values:  #\"pigs\" == \"can fly\":\n",
    "    HDcountyNo = shapeDF['countyNo'].to_list()\n",
    "else:\n",
    "    print(\"County numbers not in input file. Now I will assign each HD center to a county based on the county geoms\")\n",
    "    HDcountyNo = [-999]*nHDs\n",
    "    for t in popHDlist:\n",
    "        for c, geom in enumerate(countyGeom):\n",
    "            if geom.contains(hdCP[t]):\n",
    "                HDcountyNo[t] = c\n",
    "                break\n",
    "    unassignedList = list()\n",
    "    for t in popHDlist:\n",
    "        if HDcountyNo[t] == -999:\n",
    "            unassignedList.append(t)\n",
    "    if len(unassignedList) > 0:\n",
    "        print(\"I found\",len(unassignedList),\"populd HDs whose centers fall outside vtd-based county lines.  Assign to closest county\")\n",
    "        for t in unassignedList:\n",
    "            minDist = maxD\n",
    "            for c in range(nCounties):\n",
    "                dist = countyGeom[c].distance(hdCP[t])\n",
    "                if dist < minDist:\n",
    "                    minDist = dist\n",
    "                    HDcountyNo[t] = c\n",
    "    if len(unassignedList) > 0:\n",
    "        if len(unassignedList) > 20:\n",
    "            print(\"  (too many to plot individually)\")\n",
    "        else:\n",
    "            print(\"FYI, here are those manually assigned HDcenters to their counties\")\n",
    "            for t in unassignedList:\n",
    "                print(t,HDweight[t],\" is the HD row and its weight in the ensemble\")\n",
    "                plotPoly(hdCP[t].buffer(0.1))\n",
    "                plotCenter(int(HDvPop[t]),hdCP[t],6)\n",
    "                plotPoly(countyGeom[HDcountyNo[t]])\n",
    "                plotPoly(MAP,0.2)\n",
    "                plt.show()\n",
    "print(\"Ready to capture unit centroids based on these HD poly shapes; see next block\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3f0f8434-5f7a-41d1-bd86-07ec230b4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, let me renormalize the HDweights if there were cut districts\n",
      "our HDweight sum was 0.9999999999997582 but is now 1.0\n"
     ]
    }
   ],
   "source": [
    "print(\"now, let me renormalize the HDweights if there were cut districts\")\n",
    "sumWt = np.sum(HDweight)\n",
    "HDweight = [HDweight[t]/sumWt for t in range(nHDs) ]\n",
    "\n",
    "print(\"our HDweight sum was\",sumWt,\"but is now\",np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af22eba0-8e05-43e7-b96b-6aedab016957",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see early states for continuing w/option 1.  NC and later use option 3 ....\n",
    "#if this is a cold restart, would need to add block to read in unit topology before below block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8d5f678f-c2df-4ee7-bafe-fd75ba2cb42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\n"
     ]
    }
   ],
   "source": [
    "#OPTION 3 \n",
    "print(\"lead = 'option3' - create HDunitLists based on each HD's tractList, with in-out for multiVTD units\")\n",
    "HDtractListString = shapeDF[\"HDtractList\"]\n",
    "HDtractList = [ast.literal_eval(HDtractListString[t]) for t in range(nHDs)]\n",
    "nCCBs = len(CCBlist)\n",
    "CCBpopFrac = [[0. for j in range(nCCBs) ]  for t in range(nHDs)]\n",
    "\n",
    "unitParentVTDno = [-999 for u in range(nUnits)]\n",
    "for u in range(nUnits):    \n",
    "    if allUnits[u] %1 == 0:\n",
    "        v = allUnits[u]\n",
    "        unitParentVTDno[u] = parentVTDno[v]\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ec034144-b3dc-4b72-9350-6d85cabcb8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\n",
      "working on vtd --> unit conversion for HD 1 last HD had pop 776754.0\n",
      "working on vtd --> unit conversion for HD 1001 last HD had pop 777438.0\n",
      "working on vtd --> unit conversion for HD 2001 last HD had pop 777937.0\n",
      "working on vtd --> unit conversion for HD 4001 last HD had pop 658650.0\n",
      "working on vtd --> unit conversion for HD 5001 last HD had pop 777412.0\n",
      "working on vtd --> unit conversion for HD 9001 last HD had pop 778566.0\n",
      "working on vtd --> unit conversion for HD 10001 last HD had pop 777074.0\n",
      "working on vtd --> unit conversion for HD 14001 last HD had pop 731448.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option3, now assigning each HD's units (excluding CCB's) based on the captured whole vtd's\" )\n",
    "HDunitList = [ list() for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    if t %1000 == 1:\n",
    "        print(\"working on vtd --> unit conversion for HD\",t,\"last HD had pop\",r3( np.sum([unitPop[u] for u in HDunitList[t-1]]) ) )\n",
    "    capturedCountyPop = [0. for c in range(nCounties)]\n",
    "    capturedCCBpop = [0. for i in range(nCCBs)]\n",
    "    for v in HDtractList[t]:\n",
    "        c = HDcountyNo[v]\n",
    "        if c in range(nCounties):  #we assigned countyno = -999 to unpopulated tracts\n",
    "            if c in unitCounties:\n",
    "                capturedCountyPop[c] += tractPop[v]\n",
    "            elif c in allFusedCounties:\n",
    "                for i, L in enumerate(CCBlist):\n",
    "                    if c in L:\n",
    "                        capturedCCBpop[i] += tractPop[v]\n",
    "            else:  #this vtd not part of a unit or fused county.  Could be split into fragments\n",
    "                for u in countyUnitList[c]:\n",
    "                    if unitParentVTDno[u] == v :  #we assign all units = vtd fragments from this HDTL-captured vtd\n",
    "                        HDunitList[t].append(u)\n",
    "    for c in unitCounties:\n",
    "        if capturedCountyPop[c] > 0.5 * countyPop[c]:\n",
    "            HDunitList[t].append(allUnits.index(c+0.5))\n",
    "    for i in range(nCCBs):  #don't yet assign in/out CCBs.  We'll do so in below block\n",
    "        CCBpopFrac[t][i] = capturedCCBpop[i] / CCBpop[i]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1494814c-7b90-425c-b8b1-3a2006bdeb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\n",
      "CCBno 0 with pop 127657.0 would be used 1.188 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n",
      "CCBno 1 with pop 164779.0 would be used 1.747 if we added it to all adjoining districts\n",
      "Here is the histogram of relative district pop with these added\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if we are not allowed to use multiple CCB's in a single HD 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCBno 0 with pop 127657.0 would be used 1.1877360880855612 if we added it to all adjoining districts with no 2-CCB districts\n",
      "CCBno 1 with pop 164779.0 would be used 1.7465264425086484 if we added it to all adjoining districts with no 2-CCB districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Continuing option 3.  Determine each CCB's use if we add it to all adjoining HDs\")\n",
    "CCBwouldUse = [0. for i in range(nCCBs)]\n",
    "CCBaddCandidates = [list() for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    jNo = allUnits.index(j+0.25)\n",
    "    ccbNbrSet = set(unitNbrs[jNo])\n",
    "    withCCBpopRat = list()\n",
    "    for t in range(nHDs):\n",
    "        if len(ccbNbrSet.intersection(set(HDunitList[t]))) > 0:\n",
    "            CCBwouldUse[j] += HDweight[t] * nDistricts\n",
    "            CCBaddCandidates[j].append(t)\n",
    "            withCCBpopRat.append((np.sum([unitPop[u] for u in HDunitList[t]]) + CCBpop[j]) / aDP )\n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",r3(CCBwouldUse[j]),\"if we added it to all adjoining districts\")\n",
    "    print(\"Here is the histogram of relative district pop with these added\")\n",
    "    plt.hist(withCCBpopRat,histtype=\"step\",label=\"CCB \"+str(j))\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "avoidMultiCCBuse = int(input(\"enter 1 if we are not allowed to use multiple CCB's in a single HD\"))  #option by state\n",
    "if avoidMultiCCBuse == 1:\n",
    "    for j in range(nCCBs):  #this block -- preventing HDs from picking up multiple CCB's; instead, pick up the closest one  \n",
    "        jNo = allUnits.index(j+0.25)\n",
    "        for jj in range(j+1, nCCBs):\n",
    "            jjNo = allUnits.index(j+0.25)\n",
    "            for t in CCBaddCandidates[j].copy():\n",
    "                if t in CCBaddCandidates[j]:\n",
    "                    if t in CCBaddCandidates[jj].copy():\n",
    "                        dist_j, dist_jj = hdCP[t].distance(unitCP[jNo]), hdCP[t].distance(unitCP[jjNo])\n",
    "                        if dist_j > dist_jj:\n",
    "                            CCBaddCandidates[j].remove(t)  #for MN, don't allow any districts with two CCB's\n",
    "                            CCBwouldUse[j] -= HDweight[t] * nDistricts\n",
    "                        else:\n",
    "                            CCBaddCandidates[jj].remove(t)\n",
    "                            CCBwouldUse[jj] -= HDweight[t] * nDistricts\n",
    "for j in range(nCCBs):    \n",
    "    print(\"CCBno\",j,\"with pop\",CCBpop[j],\"would be used\",CCBwouldUse[j],\"if we added it to all adjoining districts with no 2-CCB districts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0d38eb0e-b516-4fd4-bb9e-6ca0eecfa772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\n",
      "final unit use of CCB 0 is 1.00127 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final unit use of CCB 1 is 1.00037 ; here is a histogram of HDpop using this CCB\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Continuing option 3. Now assign CCB units.  For each, work from most to least underpopped\")\n",
    "#previously, we went from most-used to least-used CCB vtds until each has unit use\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "CCBuse = [0. for i in range(nCCBs)]\n",
    "for j in range(nCCBs):\n",
    "    postCCBaddPops = list()\n",
    "    unitNo = allUnits.index(j+0.25)\n",
    "    #ccbNbrSet = set(unitNbrs[unitNo])\n",
    "    #ccbFrac = [-1.*CCBpopFrac[t][j] for t in range(nHDs) ]  # -1 to go from most- to least-used\n",
    "    #idx = np.argsort(ccbFrac)\n",
    "    preCCBpop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in CCBaddCandidates[j]]\n",
    "    idx = np.argsort(preCCBpop)\n",
    "    i = 0\n",
    "    t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "    while CCBuse[j] < 1.00  and i < len(CCBaddCandidates[j]) :  #and CCBpopFrac[t][j] > 0:\n",
    "        t = CCBaddCandidates[j][idx[i]] #idx[i]\n",
    "        CCBuse[j] += HDweight[t] * nDistricts\n",
    "        HDunitList[t].append(unitNo)\n",
    "        postCCBaddPops.append(np.sum([unitPop[u] for u in HDunitList[t] ]))\n",
    "        i +=1\n",
    "    unitUse[unitNo] = CCBuse[j]\n",
    "    print(\"final unit use of CCB\",j, \"is\",r5(CCBuse[j]),\"; here is a histogram of HDpop using this CCB\")\n",
    "    plt.hist(postCCBaddPops)\n",
    "    plt.axvline(aDP,ls=\"--\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf747d9c-cf6d-420e-9c3c-6723619586ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end of preliminaries for \"option 3\" - converting HDtractlists to unit lists instead of option 2's geometric probing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26102ca2-79c1-48db-b873-7fa3a1553911",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dc26d08c-a5d9-4e7e-9da5-ae98cf658a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the histogram of HD pops relative to aDP\n"
     ]
    },
    {
     "data": {
      "image/png": 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1dUVlZWV0dnZGRUVFPuMCwIBqlmwalPPuXTVnUM57Jjodz995XVHp6emJ7du3R319/dsnKC6O+vr6aGtrO6VzHDlyJN566614//vfn9+kAMBZZ1Q+Ox88eDB6e3ujqqqq3/aqqqp46aWXTukcd9xxR0yePLlf7LxTd3d3dHd39/27q6srnzEBgBFiSD/1s2rVqtiwYUM8/fTTUV5efsL9mpubo7Kysu9WXV09hFMCAKnIK1TGjx8fJSUl0dHR0W97R0dHTJw48aTH3n///bFq1ar45S9/GZdccslJ9126dGl0dnb23fbv35/PmADACJFXqJSWlsaMGTOitbW1b1sul4vW1taoq6s74XH33Xdf3HPPPbFly5aYOXPmu95PWVlZVFRU9LsBAGefvN6jEhHR2NgYCxcujJkzZ8asWbNizZo1cfjw4Vi0aFFERCxYsCCmTJkSzc3NERHx/e9/P5YvXx6PPvpo1NTURHt7e0REvO9974v3ve99p/GhAAAjTd6h0tDQEAcOHIjly5dHe3t7TJ8+PbZs2dL3Btt9+/ZFcfHbF2p+9KMfRU9PT3z5y1/ud56mpqb4zne+896mBwBGtLy/R2U4+B4VAE4336My+Ib8e1QAAIaSUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZo4Z7AADg1NQs2TQo5927as6gnPd0cEUFAEiWKyoAcBoN1lWPs5UrKgBAsoQKAJAsL/0AkCwvo+CKCgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkq6BQaWlpiZqamigvL4/a2trYtm3bSfd/4oknYtq0aVFeXh4XX3xxbN68uaBhAYCzS96hsnHjxmhsbIympqbYsWNHXHrppTF79ux4/fXXB9z/+eefj+uuuy5uuOGG2LlzZ1xzzTVxzTXXxJ/+9Kf3PDwAMLIVZVmW5XNAbW1tfPrTn44HHnggIiJyuVxUV1fHN77xjViyZMlx+zc0NMThw4fjF7/4Rd+2z3zmMzF9+vRYu3btKd1nV1dXVFZWRmdnZ1RUVOQzLgBnMF/4NjQG69eTT8fzd17fTNvT0xPbt2+PpUuX9m0rLi6O+vr6aGtrG/CYtra2aGxs7Ldt9uzZ8cwzz5zwfrq7u6O7u7vv352dnRHxvwcMwNkj131kuEc4KwzW8+ux8+Z5TaSfvELl4MGD0dvbG1VVVf22V1VVxUsvvTTgMe3t7QPu397efsL7aW5ujhUrVhy3vbq6Op9xAYBTULlmcM9/6NChqKysLOjYJH/rZ+nSpf2uwuRyuXjjjTfiAx/4QBQVFZ22++nq6orq6urYv3+/l5TyZO0KY90KY90KY90KZ+0K8851y7IsDh06FJMnTy74nHmFyvjx46OkpCQ6Ojr6be/o6IiJEycOeMzEiRPz2j8ioqysLMrKyvptGzt2bD6j5qWiosIfYoGsXWGsW2GsW2GsW+GsXWH+/7oVeiXlmLw+9VNaWhozZsyI1tbWvm25XC5aW1ujrq5uwGPq6ur67R8R8atf/eqE+wMAHJP3Sz+NjY2xcOHCmDlzZsyaNSvWrFkThw8fjkWLFkVExIIFC2LKlCnR3NwcERG33XZbXHXVVfGDH/wg5syZExs2bIg//vGP8dBDD53eRwIAjDh5h0pDQ0McOHAgli9fHu3t7TF9+vTYsmVL3xtm9+3bF8XFb1+oueyyy+LRRx+Nu+66K+6888742Mc+Fs8880xcdNFFp+9RFKisrCyampqOe5mJd2ftCmPdCmPdCmPdCmftCjMY65b396gAAAwVv/UDACRLqAAAyRIqAECyhAoAkKwRHyotLS1RU1MT5eXlUVtbG9u2bTvp/k888URMmzYtysvL4+KLL47NmzcP0aRpyWfd1q1bF1deeWWMGzcuxo0bF/X19e+6ziNZvn9zx2zYsCGKiorimmuuGdwBE5Xvur355puxePHimDRpUpSVlcX5559/Vv73mu+6rVmzJj7+8Y/HOeecE9XV1XH77bfHf//73yGaNg2/+93vYu7cuTF58uQoKio66W/PHbN169b41Kc+FWVlZfHRj340fvrTnw76nKnJd92eeuqp+PznPx8f/OAHo6KiIurq6uLZZ5/N/46zEWzDhg1ZaWlptn79+uzPf/5zdtNNN2Vjx47NOjo6Btz/97//fVZSUpLdd9992Ysvvpjddddd2ejRo7MXXnhhiCcfXvmu27x587KWlpZs586d2e7du7OvfOUrWWVlZfa3v/1tiCcffvmu3TGvvfZaNmXKlOzKK6/MvvSlLw3NsAnJd926u7uzmTNnZldffXX23HPPZa+99lq2devWbNeuXUM8+fDKd90eeeSRrKysLHvkkUey1157LXv22WezSZMmZbfffvsQTz68Nm/enC1btix76qmnsojInn766ZPuv2fPnuzcc8/NGhsbsxdffDH74Q9/mJWUlGRbtmwZmoETke+63Xbbbdn3v//9bNu2bdkrr7ySLV26NBs9enS2Y8eOvO53RIfKrFmzssWLF/f9u7e3N5s8eXLW3Nw84P7XXnttNmfOnH7bamtrs69+9auDOmdq8l23dzp69Gg2ZsyY7Gc/+9lgjZisQtbu6NGj2WWXXZb9+Mc/zhYuXHhWhkq+6/ajH/0omzp1atbT0zNUIyYp33VbvHhx9rnPfa7ftsbGxuzyyy8f1DlTdipPuN/+9rezT3ziE/22NTQ0ZLNnzx7EydJ2Kus2kAsvvDBbsWJFXseM2Jd+enp6Yvv27VFfX9+3rbi4OOrr66OtrW3AY9ra2vrtHxExe/bsE+4/EhWybu905MiReOutt+L973//YI2ZpELX7rvf/W5MmDAhbrjhhqEYMzmFrNvPf/7zqKuri8WLF0dVVVVcdNFFsXLlyujt7R2qsYddIet22WWXxfbt2/teHtqzZ09s3rw5rr766iGZ+UzlueH0yOVycejQobyfG5L89eTT4eDBg9Hb29v3jbnHVFVVxUsvvTTgMe3t7QPu397ePmhzpqaQdXunO+64IyZPnnzcf9gjXSFr99xzz8XDDz8cu3btGoIJ01TIuu3Zsyd+85vfxPXXXx+bN2+OV199Nb72ta/FW2+9FU1NTUMx9rArZN3mzZsXBw8ejCuuuCKyLIujR4/GLbfcEnfeeedQjHzGOtFzQ1dXV/znP/+Jc845Z5gmO7Pcf//98e9//zuuvfbavI4bsVdUGB6rVq2KDRs2xNNPPx3l5eXDPU7SDh06FPPnz49169bF+PHjh3ucM0oul4sJEybEQw89FDNmzIiGhoZYtmxZrF27drhHS9rWrVtj5cqV8eCDD8aOHTviqaeeik2bNsU999wz3KMxwj366KOxYsWKePzxx2PChAl5HTtir6iMHz8+SkpKoqOjo9/2jo6OmDhx4oDHTJw4Ma/9R6JC1u2Y+++/P1atWhW//vWv45JLLhnMMZOU79r95S9/ib1798bcuXP7tuVyuYiIGDVqVLz88stx3nnnDe7QCSjkb27SpEkxevToKCkp6dt2wQUXRHt7e/T09ERpaemgzpyCQtbt7rvvjvnz58eNN94YEREXX3xxHD58OG6++eZYtmxZv99p420nem6oqKhwNeUUbNiwIW688cZ44oknCrrSPmL/KktLS2PGjBnR2traty2Xy0Vra2vU1dUNeExdXV2//SMifvWrX51w/5GokHWLiLjvvvvinnvuiS1btsTMmTOHYtTk5Lt206ZNixdeeCF27drVd/viF78Yn/3sZ2PXrl1RXV09lOMPm0L+5i6//PJ49dVX+8IuIuKVV16JSZMmnRWRElHYuh05cuS4GDkWe5mffTshzw2Fe+yxx2LRokXx2GOPxZw5cwo7Sd5v2T2DbNiwISsrK8t++tOfZi+++GJ28803Z2PHjs3a29uzLMuy+fPnZ0uWLOnb//e//302atSo7P777892796dNTU1nbUfT85n3VatWpWVlpZmTz75ZPbPf/6z73bo0KHhegjDJt+1e6ez9VM/+a7bvn37sjFjxmRf//rXs5dffjn7xS9+kU2YMCH73ve+N1wPYVjku25NTU3ZmDFjssceeyzbs2dP9stf/jI777zzsmuvvXa4HsKwOHToULZz585s586dWURkq1evznbu3Jn99a9/zbIsy5YsWZLNnz+/b/9jH0/+1re+le3evTtraWk5Kz+enO+6PfLII9moUaOylpaWfs8Nb775Zl73O6JDJcuy7Ic//GH24Q9/OCstLc1mzZqV/eEPf+j736666qps4cKF/fZ//PHHs/PPPz8rLS3NPvGJT2SbNm0a4onTkM+6feQjH8ki4rhbU1PT0A+egHz/5v6/szVUsiz/dXv++eez2trarKysLJs6dWp27733ZkePHh3iqYdfPuv21ltvZd/5zney8847LysvL8+qq6uzr33ta9m//vWvoR98GP32t78d8P+zjq3VwoULs6uuuuq4Y6ZPn56VlpZmU6dOzX7yk58M+dzDLd91u+qqq066/6kqyjLX+wCANI3Y96gAAGc+oQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsv4PoJPaZ8W489MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig unit avg and sd usage are 1.00647 0.15058\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs) ]\n",
    "print(\"Here is the histogram of HD pops relative to aDP\")\n",
    "plt.hist([HDvPop[t] / aDP for t in range(nHDs) ], bins=20, weights = HDweight, label= \"HDpop / target\" )\n",
    "plt.show()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53e4c298-e6cb-4fee-a511-b8f6c3940539",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c4183011-724a-4014-8c94-f9f9382c7e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Did I grossly overuse any units, or skip any live ones?\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above: underused < 0.5; below overused > 1.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Did I grossly overuse any units, or skip any live ones?\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.5 and unitPop[u] > 5:\n",
    "        plotPoly(unitGeom[u])\n",
    "        #print(u,unitPop[u], unitUse[u])\n",
    "        #for c in range(nCounties):\n",
    "        #    if u in countyUnitList[c]:\n",
    "        #        print(u,\"is in county\",c)\n",
    "        #        print(\"its neighbor units are\",unitNbrs[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"above: underused < 0.5; below overused > 1.6\")\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 1.6:\n",
    "        plotPoly(unitGeom[u],0.3)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ce9292b0-7d6a-419a-9491-d0def779ad04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 6033 populated HDs out of 14191\n"
     ]
    }
   ],
   "source": [
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if len(HDunitList[t]) > 0:\n",
    "        popHDlist.append(t)\n",
    "populatedTractList = popHDlist.copy()\n",
    "print(\"there are a total of\",len(populatedTractList),\"populated HDs out of\",nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f7f36559-7f1d-4203-8b24-0e8cc2ce1753",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this will show if we had any duplicated units in HDunitLists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this will show if we had any duplicated units in HDunitLists\")\n",
    "excess = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    excess[t] = len(HDunitList[t]) - len(set(HDunitList[t]))\n",
    "plt.hist(excess)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "27bc7f0a-d7ff-4c68-830f-29ba5013c314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#popHDlist = populatedTractList.copy()  #somehow our popHDlist started to include the cut District HD starters\n",
    "HDunitList = [list(set(HDunitList[t])) for t in range(nHDs)]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "af8a4945-40c2-40df-8dd4-d516f4de2f0f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 701 time is now 92\n",
      "working on HD 1403 time is now 194\n",
      "working on HD 2925 time is now 279\n",
      "working on HD 4446 time is now 400\n",
      "working on HD 5148 time is now 521\n",
      "working on HD 8725 time is now 624\n",
      "working on HD 9431 time is now 762\n",
      "working on HD 13048 time is now 871\n",
      "out of 6033 total HDs, there were 4617.0 contiguous and 1119.0 complement-contiguous HDs\n",
      "1184 HDs had both discontiguity problems, while enclave-only = 3730 and discontig only= 232\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 1416 3730\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "40f06e44-45be-4a36-b62c-26698af70e06",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 738641 district pop vs 777517 target\n",
      "we'll also trim any HD with total islands' pop <= 15550\n",
      "working on HD 1\n",
      "working on HD 844\n",
      "working on HD 2617\n",
      "working on HD 3869\n",
      "working on HD 5060\n",
      "working on HD 8772\n",
      "working on HD 9678\n",
      "working on HD 14132\n",
      "Out of 1416 discontig HDs 1416 had discontig HDs.\n",
      "Of these, 1249 won't be under 738641 after all minor discontigys were shed, while 167 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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iv3nwl5ZfmbHlBB7qoMWuFL3UO2XzByI0vpj9aAfmN7lJIUp2G1XgkUceQXh4OFauXCltGzRoEPz8/LB27VqnxwwYMAB5eXnYvXu3tK1ly5aYPHkyJk+e7PSYZcuW4c0334Rer4dKpQIATJ8+HV9++SVOnz4NAHjmmWeQn5+P7du3S8f17NkTXbp0wfLlyyGEQGRkJF599VW89tprAACDwYDw8HCsWbMGgwcPrvB+jUYjNBoNDAYDAgMDK2xPRER0K9jzm34+dwVL9p6rsP0jd0Zgx3GdQ5Blz2yqawni1f397daw3d13343du3fj999/BwAcO3YM+/btQ79+/Zy2z8jIwI4dOzBq1CiHffPnz0dISAi6du2KRYsWobi4WNqXlJSE++67TwqcACAuLg5nzpxBTk6O1CY2NlZ2zri4OCQlJQEAUlNTodfrZW00Gg2io6OlNqWZTCYYjUbZg4iIyJPsPK7D3975HkM++dWlwAmA08AJuLH6ypxtKRzCc4Nbw3bTp0+H0WhE+/bt4eXlBYvFgnfeeQdDhw512v7TTz9Fw4YNMXDgQNn2V155Bd26dUOjRo3wyy+/YMaMGdDpdHjvvfcAAHq9HlFRUbJjwsPDpX3BwcHQ6/XStpJt9Hq91K7kcc7alDZv3jzMmTPHlbeCiIjolpu3MwX/+inV7ePKC4sEAJ2hEAdSs5k47iK3gqdNmzZh3bp1+Oyzz3DHHXfg6NGjmDx5MiIjIzF8+HCH9qtWrcLQoUMd8oumTp0q/fvOO++ESqXCyy+/jHnz5kGtVlfyVm7ejBkzZNdmNBrRrFmzGrseIiIiu53HL1cqcHJVZl5hxY0IgJvB0+uvv47p06dL+UKdOnXC+fPnMW/ePIfg6X//+x/OnDmDjRs3Vnje6OhoFBcXIy0tDe3atYNWq0VGRoasjf25VquV/uusTcn99m0RERGyNl26dHF6HWq1ukaDNyIiImcsVoGZXyVX62twHTvXuZXzVFBQAKVSfoiXlxesVqtD25UrV6J79+7o3Llzhec9evQolEolwsLCAAAxMTH46aefUFRUJLXZtWsX2rVrh+DgYKlNySR0e5uYmBgAQFRUFLRarayN0WjE/v37pTZERES1wYHUbGTnF1XcsAxKhWMlcTsFuI6du9wKnh599FG888472LFjB9LS0rB161a89957eOKJJ2TtjEYjNm/ejBdffNHhHElJSfjggw9w7Ngx/Pnnn1i3bh2mTJmC5557TgqMnn32WahUKowaNQonT57Exo0b8eGHH8qG1CZNmoTExET885//xOnTp5GQkIBDhw5hwoQJAGz1piZPnoy3334bX3/9NU6cOIFhw4YhMjISAwYMcPd9IiIiuuUsVoGkP7LwTbKu4sZlUAAYfW+U9O/S+wCuY+cut0oV5OXlIT4+Hlu3bkVmZiYiIyMxZMgQzJo1SzYzbsWKFZg8eTJ0Oh00Go3sHEeOHMG4ceNw+vRpmEwmREVF4fnnn8fUqVNlQ2bHjx/H+PHjcfDgQYSGhmLixImYNm2a7FybN2/GzJkzkZaWhjZt2mDhwoV4+OGHpf1CCMyePRsrVqxAbm4uevXqhaVLl6Jt27Yu3S9LFRARUU0pWTX8Zrx8XxRmPNzB6fnqap2n6v7+dit4qm8YPBERUU2wVw2/2S9oBQCtxhf7pj0IL6VCqg9V19exq+7vb65tR0RE5EHKqhpeGaXLEHgpFSxHUAUYPBEREdUQZz1BB1Kzb3qorjSWIahaDJ6IiIhqQFk5SP06aqv8tViGoGoxeCIiIrrFyspp0hsKsernNJfO0UDtjaum4nLb2HOeWIagarlVqoCIiIhujrnYir9vPVHuWnPlpXArAGgD1QhQeVX4WgIsQ1AdGDwRERHdIonJOvSct7vCgpdlJYvbQ6AhPZojI89U4etNiW1T58oQeAIO2xEREVUzi1VgyZ5zeP/732/qPNrrdZlMxY4rezjTMjTgpl6PnGPwREREVNUKDYDpKqBpgsRkHRK+Pgm90dZTpEUW8uGHPPi7dKqQABVm9r8dWo2fVJcp6Y8sl45lonj1YPBERERUlQoNwNpBQP5f+CFmNcZu0UnDcBHIwgbVXGQhEMPN010KoLLyzdBq/GT1mXpENUKExhd6Q6HTIT4milcv5jwRERFVJdNVIP8vICcNrXcOhha2XiJ74NRCmYkQGBGAay6fsnSdJi+lArMf7QCA69XVBAZPREREVUnTBBixA4UNmqMpMrBBNRfdFL9LgdN5axgGm+Ohh+uVvp0Nv/XtGIFlz3WDViPfp9X4Ytlz3ZgoXo24tl05uLYdERFV1re/HEL7xCFoocyUttkDJ50bgVNIgAoH3owtsxepvqxX5w6ubUdERFSL2IOZE3kN8K+icdiiTpD2TSka51bgBAADuzUpNxjienW3HoftiIiIqkhisg69FuzBkE9+xRd7D+B9n6Wy/e/7LEUEXJspZ7f9uA4WKweJPAmDJyIioipgX3JFZyiUJYeft4ZhoCkB561haKHMxAbVXLcCKJ2hEAdSs6vxysldDJ6IiIhuksUqMGdbCgRsdZxKJ4cfEW0x2BwvBVCb1G9Ls/BcUXq2HdUs5jwRERFVQslE7St5JugMtgAnH37IQiBghSw5XIcQDDbHY4NqLpQNQ5Fv8nP5tVjs0rMweCIiInJTYrIOc7alSAFTSXnwx3DzdATgmkM5AnsA5XOtIfKgcum1Iljs0uMweCIiInKRq2vU5cG/zOrhOoQABUCjABVy8s1lLgIM2Apestil52HOExERkQsSk3W4Z/7um17c125Al0gAjhXC7YL9fVjs0kOx54mIiKgC9pl0VVkw4KEOWvSIauQw/Bfk54MX7mmJCQ+2YY+Th2LwREREVI6SM+mqij2PyUupwEMdtKwQXssweCIiIiqp0GBb3FfTBABwIDVb6hnSIgv58Cszn8lVj3WOkAIkVgivfZjzREREZFdoANYOAtY8DBjSAQC7UvQAgAhkYaNqLj5VzUdDFNzUy3x9jFXDazMGT0RERIAtcLpyDsj/C8hJA9b0hyXnIjYdSsedOIeNqjloocxECIwIwLWbeilWDa/dOGxHRESUexHYONQWQA36N/DFi0BOGrKX9sHD5ofxrnoVvBVWXLCGYrA53qF+U2WwanjtxZ4nIiKq3woNwIahQMZJW4/TFy8Cg/6N/IBmaFx0GQvV/4a3wopiocQE8ytSxfCbxarhtReDJyIiqt9MVwGTAbAWA0pvICcN4osXsTK/l6zZ380jcRytb/rlFGDV8NqOwRMREdU/hQbAcMn2b00TYMQOILjl9QDKC4qcNLyC9bJDxvt8jQg3FvN1xl6AgFXDazcGT0REVL84mVEHTVNbAKVpBlgtsubzzc/gvDUMLZSZ2KCae1MBlFbjy6rhdQCDJyIiql9MV2Uz6qQACgDM+Q7Nh3jvxSvmCbIASutmABXk74N1o6Kxb9qDDJzqAAZPRERUv5QcprMHUBf2A//uDVyTlw8oEkq0UGZisWqJFEBlIRD58HP55RQA5g/shHvahHKoro5QCCFYpasMRqMRGo0GBoMBgYGBNX05RERUlQzptsApJ02+XeEFPPI+8vcsQkD+RRQJJXwUVpy3hmGCeQLSEOlyhfGQABXeeaIje5tuser+/mbPExER1U+apsATK+TbFF7AqG+B7sNxOm49zlvD4KOwokgoYYS/W4FTowAfJM3ozcCpDmLwRERE9ZMhHdj6kmxToToUlgAtACDd2giDzfE4bw3DadEcL5mnurWm3btPdILKm1+zdRE/VSIiqptKliMoLf0QsLofkJOGdITjOdN0pFtD4FuYgcsf9sbKHf9DaIAaOoTgGXM8njXPhA6hLr/0ksFd2eNUhzF4IiKiusdZOQK79EPAqjgg9wIuWEPxVOFM7BN34ilzAs5bw9AMGYjdPxJz1u1CkL8PMhDiVo/T6Huj8EiXyCq+IfIkDJ6IiKjuKascgSEd+PwFwFqMYnjJllvRIUQapstCIHSF3sgtKIKrs6oUAF6+Lwpv9u9QHXdEHoSz7crB2XZERLVYydl0wS2BJ1ZAbH0Jipw0GNQReN4w1ulyK1pkIR9+Um9TA7UXrposDu1K++/IHri3beMqvgmqDI+abWexWBAfH4+oqCj4+fmhVatWmDt3LkrGXyNGjIBCoZA9+vbtKztPdnY2hg4disDAQAQFBWHUqFG4evWqrM3x48dx7733wtfXF82aNcPChQsdrmfz5s1o3749fH190alTJ+zcuVO2XwiBWbNmISIiAn5+foiNjcXZs2fduWUiIqqt7FXD7fWcVvWBIicN561h6Gv4e5nr1OlLDdO5EjgBQHaBuQoummoDt4KnBQsWYNmyZViyZAlOnTqFBQsWYOHChfjoo49k7fr27QudTic91q+Xrw80dOhQnDx5Ert27cL27dvx008/4aWXbsx4MBqN6NOnD1q0aIHDhw9j0aJFSEhIwIoVN6aU/vLLLxgyZAhGjRqF3377DQMGDMCAAQOQnJwstVm4cCEWL16M5cuXY//+/QgICEBcXBwKCwvdepOIiKiWclKOYErROGmoriqFNfSt8nOSZ3Jr2O6RRx5BeHg4Vq5cKW0bNGgQ/Pz8sHbtWgC2nqfc3Fx8+eWXTs9x6tQpdOjQAQcPHsRdd90FAEhMTMTDDz+M9PR0REZGYtmyZXjzzTeh1+uhUqkAANOnT8eXX36J06dPAwCeeeYZ5OfnY/v27dK5e/bsiS5dumD58uUQQiAyMhKvvvoqXnvtNQCAwWBAeHg41qxZg8GDB1d4vxy2IyKqPSxWgQOp2cjMK0RYQ1/0iGoEr7xLEKv7Q5GbJrU7bw3DYHO8WwFUowAVcvLNTvOfFLCtWbdv2oOsIO4hPGrY7u6778bu3bvx+++/AwCOHTuGffv2oV+/frJ2P/zwA8LCwtCuXTuMHTsWWVk31gBKSkpCUFCQFDgBQGxsLJRKJfbv3y+1ue+++6TACQDi4uJw5swZ5OTkSG1iY2NlrxsXF4ekpCQAQGpqKvR6vayNRqNBdHS01IaIiOqGxGQdei3YgyGf/IpJG45iyCe/YuD8TchZ2geKXNtQ3UBTQqUW+I3Q+OLtxzsCsAVKJdmfz360AwOnesSt4Gn69OkYPHgw2rdvDx8fH3Tt2hWTJ0/G0KFDpTZ9+/bFf/7zH+zevRsLFizAjz/+iH79+sFisY0Z6/V6hIWFyc7r7e2NRo0aQa/XS23Cw8NlbezPK2pTcn/J45y1Kc1kMsFoNMoeRETk2RKTdRi79gh0hhspGVpkYXHhTASbLkk9TUdEW2k2nTsL/Mb374CH74zAsue6QauRD81pNb5Y9lw31nSqZ7zdabxp0yasW7cOn332Ge644w4cPXoUkydPRmRkJIYPHw4AsuGwTp064c4770SrVq3www8/oHfv3lV79VVs3rx5mDNnTk1fBhERucBiFfj1zyxM/+KEw3BaPvyQhUDACtkQnb0cwQbVXJcX+A0OsI2C9O0YgYc6aB2HBtnjVO+4FTy9/vrrUu8TYAuOzp8/j3nz5knBU2m33XYbQkNDce7cOfTu3RtarRaZmZmyNsXFxcjOzoZWayuJr9VqkZGRIWtjf15Rm5L77dsiIiJkbbp06eL0WmfMmIGpU6dKz41GI5o1a1b2G0JERDUiMVmHOdtSZL1NJeXBH8PN0xGAa9CXym2yVw0vWY6gPJl5N17DS6lATKuqTzan2sWtYbuCggIolfJDvLy8YLVayzwmPT0dWVlZUgATExOD3NxcHD58WGqzZ88eWK1WREdHS21++uknFBUVSW127dqFdu3aITg4WGqze/du2Wvt2rULMTExAICoqChotVpZG6PRiP3790ttSlOr1QgMDJQ9iIjIszgbpnMmD/4OgZNd6XIE5eEsOirNreDp0UcfxTvvvIMdO3YgLS0NW7duxXvvvYcnnngCAHD16lW8/vrr+PXXX5GWlobdu3fj8ccfR+vWrREXFwcAuP3229G3b1+MHj0aBw4cwM8//4wJEyZg8ODBiIy0lbN/9tlnoVKpMGrUKJw8eRIbN27Ehx9+KOsVmjRpEhITE/HPf/4Tp0+fRkJCAg4dOoQJEyYAABQKBSZPnoy3334bX3/9NU6cOIFhw4YhMjISAwYMqIr3joiIboVCA5B5GjBcgsUqMGdbijRMp0UWWiEdDVFQLS8dobENzRGV5Fapgry8PMTHx2Pr1q3IzMxEZGQkhgwZglmzZkGlUuHatWsYMGAAfvvtN+Tm5iIyMhJ9+vTB3LlzZYnb2dnZmDBhArZt2walUolBgwZh8eLFaNCggdTm+PHjGD9+PA4ePIjQ0FBMnDgR06ZNk13P5s2bMXPmTKSlpaFNmzZYuHAhHn74YWm/EAKzZ8/GihUrkJubi169emHp0qVo27atS/fLUgVERDWs0AD8ZwCQcRJo0BiHe2/AoM8uAAAikIXNqjlorMjFKdEMz5vfdGsNOlcsZzJ4rVTd399cnqUcDJ6IiGqY4ZJtEV/DRQBArk84+uXNBABsVs1BU+UVAEC6NQRPmhPKHKYrSQFIPVcl/11SkL8P5g/sxMCplmLwVIMYPBEReQBDOrCqrxRA6a3BgALQKmx1/9KtIXjKnOBy0csIjS9mP2pbvLd00nmQvw9euDsKEx5szVl0tVh1f3+7NduOiIjoltM0hWXEN8j88H5E4Aq0yhxplzuB07CYFujXMUJWXoClB6gyGDwREZHHO5Dtj0WmCdiiTpBtf6Vooss9Tv06RjiUGWDpAaoMt2bbERER1QRjRioW+yxx2L7Y56MKl1lRgLPmqGoxeCIiIs9mSMf9Pz8vJYfrrcHQC1vNv6bKLGxWJZQZQHHtOaoODJ6IiMhzGS4Bq/pCnX8ZgC3H6QnzW3jC9BbSraEAbgRQztap49pzVB2Y80RERJ5L3QAiIBRmgx5/WQNlyeFPmWdLdZ6ulFqnzllyOFFVYfBEREQewWIVjjPffDU4eO9q/P3T73AVfrI6TjqE4EnzbATgGjLRSFYg01lyOFFVYfBEREQ1omSwlHYlH+sPXIDeaJL22+sxHUgtwDk0dXoOZ0UxmRxO1Y3BExER3XI7j+sw86tkZOeby2yjNxRizNojaKB276vqVieHO+0x41BhncbgiYiIbql3dqTgk/+lVtjOvvzFVVOxS+cN9vfBvFu8pEpiss6hSrm9x4xJ6nUXZ9sREdEt886Oky4FTu7qe0c4Ds186JYHTmPXHpEFToCtx2zs2iNITNbdsmuhW4vBExER3RI7j+vwyf/SquXcw++OuuVDdXO2pThdVNi+bc62FFisXD62LmLwRERE1c5iFXjzyxNVft6aqh5+IDXbocepJAFAZyjEgdTsW3dRdMsweCIiomq3ZM855BQUuX2cAkCQvw8UuFEtvOQ+oGaqh2fmlR04VaYd1S5MGCciomqVmKzD+9//7vZx9nBo/sBOAOCQmK2twcTssIa+VdqOahcGT0REVG3suUGVUTo4eqiD1mNKAvSIaoQIjS/0hkKneU8K2K6f9abqJgZPRERUbSrKDSqtazMNRtwT5TQ48lIqPKZquJdSgdmPdsDYtUegAGQBFBcjrvuY80RERNXG3ZyfN/rejse7NEFMqxCPDzz6dozAsue6QauRD81xMeK6jz1PRERUbdzJ+Qny96l1w1x9O0Z41HAi3RoMnoiIqEqVXK6kkb8KDX29kVdYcZXwF25xraaq4knDiXRrMHgiIqJKK72uW06+CXN3nHIrzwmw9TpNeLB1NV0lUdVi8ERERJXibF23ypo/sFOt7HWi+onBExERuc2+rtvNLj7CRXSpNmLwREREbilvXTd3xPe/HSPuqZ15TlS/sVQBERG5xd3aTWUJbahm4ES1EoMnIiJyS1Wt18alS6i24rAdERG55WaDHi5dQrUde56IiMhRoQEwXJJtslgFkv7IQm5GGpr7myt1Wi5dQnUBe56IiEiu0ACsHQTk/wWM2AFomkplCWC4hA2qufgAgRiO6ciDv1unLr3YL1FtxOCJiIjkTFdtgVNOGrCmP36IWY2xW3TQIgsbVHPRQpkJWIEAXHMreJrwQGtMeagte5yo1uOwHRERyWma2HqcglsCOWlovXMwuip+lwKn89YwDDbHQ48QKAAE+rr2d/g9rUMZOFGdwOCJiIgcc5w0TYERO1DYoDmaIgNb1AmywEkH21puAoCxsBiNAlQoKyxSwFYMkwniVFcweCIiqu9yLwKfPgqseRgwpN/YrmmKU61GyZpOKRonBU4lDegSCQAOARQTxKkuYvBERFSf5V4E1j0FZJyUcpwsOReR9EcWDm9djC7HZsuav++zFBHIcjjNQx20WPZcN2g18jIGWo0vlj3XjQniVKcwYZyIqL7KvQh89hSQdRawFgNKbyAnDboPe+OQKRoTVF9DoQCEAKaZX8R4n6/RQpmJDaq50tBdyZpNXkoFHuqgxYHUbGTmFSKs4Y3tRHWJQghxs8sT1VlGoxEajQYGgwGBgYE1fTlERFWn0GAbqss4eSNwshajSCjho7BKzYQAXjONxhd4ABElZtuVTBpnzxJ5mur+/uawHRFRfWJPDDddtf27ROBUDHngBABLzI/hCzwAANArQjDYHI/z1jBkIRABgUEMnKhe4rAdEVF9Ubr45YgdwJr+QE4ahMIL3sLicMhjPr/iM/ND0CEEQgD3dOuM0802IDioEb5t15JDclQvseeJiKi+KFX8EsD1CuLNoHASOBUJpZTjZE8S//5UBmKju6HH7VEMnKjecit4slgsiI+PR1RUFPz8/NCqVSvMnTsX9rSpoqIiTJs2DZ06dUJAQAAiIyMxbNgwXL58WXaeli1bQqFQyB7z58+XtTl+/Djuvfde+Pr6olmzZli4cKHD9WzevBnt27eHr68vOnXqhJ07d8r2CyEwa9YsREREwM/PD7GxsTh79qw7t0xEVHeUKn6JNf2vD+HlOTS15z6VDKC0yELutSIcSM2+5ZdO5EncCp4WLFiAZcuWYcmSJTh16hQWLFiAhQsX4qOPPgIAFBQU4MiRI4iPj8eRI0ewZcsWnDlzBo899pjDud566y3odDrpMXHiRGmf0WhEnz590KJFCxw+fBiLFi1CQkICVqxYIbX55ZdfMGTIEIwaNQq//fYbBgwYgAEDBiA5OVlqs3DhQixevBjLly/H/v37ERAQgLi4OBQWFrr9RhER1UplFL+UAqhVfYDCXNkhJQMn+3+N8Ec+/AAAmXn8HUr1m1uz7R555BGEh4dj5cqV0rZBgwbBz88Pa9eudXrMwYMH0aNHD5w/fx7NmzcHYOt5mjx5MiZPnuz0mGXLluHNN9+EXq+HSqUCAEyfPh1ffvklTp8+DQB45plnkJ+fj+3bt0vH9ezZE126dMHy5cshhEBkZCReffVVvPbaawAAg8GA8PBwrFmzBoMHD67wfjnbjohqNScL/EpStgGbnpOeCgDvmgbjOZ89aKHMlAVO50QkRprfgA6hAID1o3sippVjoUwiT+FRs+3uvvtu7N69G7///jsA4NixY9i3bx/69etX5jEGgwEKhQJBQUGy7fPnz0dISAi6du2KRYsWobi4WNqXlJSE++67TwqcACAuLg5nzpxBTk6O1CY2NlZ2zri4OCQlJQEAUlNTodfrZW00Gg2io6OlNqWZTCYYjUbZg4io1jJdhbie45T/r744fPwELFYBpB8ENg+XN1WHYru4R5pNV1bg1CjAh8usUL3nVvA0ffp0DB48GO3bt4ePjw+6du2KyZMnY+jQoU7bFxYWYtq0aRgyZIgs8nvllVewYcMG7N27Fy+//DLeffddvPHGG9J+vV6P8PBw2bnsz/V6fbltSu4veZyzNqXNmzcPGo1GejRr1qzC94SIyFPtPK/Ew4bpOG8NQ0DBRYR+PhB/f3cerCvjAHuCeEBjoKEWvqYr2Oz7NgBIAdRp0VwWOAHA2493ZKI41XtulSrYtGkT1q1bh88++wx33HEHjh49ismTJyMyMhLDh8v/iikqKsLTTz8NIQSWLVsm2zd16lTp33feeSdUKhVefvllzJs3D2q1+iZu5+bMmDFDdm1Go5EBFBHVSvN2puBfP6UCCMRgxEvFLRcULwAAWOCFU71X4/ZO3eGlALCmP5rmpGGDai6eMcfjGXM88uGHPPhL53z5vig8fGdkzdwQkQdxq+fp9ddfl3qfOnXqhOeffx5TpkzBvHnzZO3sgdP58+exa9euCscbo6OjUVxcjLS0NACAVqtFRkaGrI39uVarLbdNyf0lj3PWpjS1Wo3AwEDZg4iottl5/PL1wMlGhxBMKRonazPW9Aoe2eGNXsvOIPGil5RErgmNQIPAIOgRIgVOIQEqLH22G2Y83OGW3geRp3Kr56mgoABKpTze8vLygtV6oyKtPXA6e/Ys9u7di5CQipMKjx49CqVSibCwMABATEwM3nzzTRQVFcHHxwcAsGvXLrRr1w7BwcFSm927d8uSznft2oWYmBgAQFRUFLRaLXbv3o0uXboAsPUk7d+/H2PHjnXntomIag2LVWDmV8mybRHIwvs+S2Xb3vRZhxPm26A3hGDs2iO2SuEjdiJI3QDfqgK5Ph1ROdzqeXr00UfxzjvvYMeOHUhLS8PWrVvx3nvv4YknngBgC5yefPJJHDp0COvWrYPFYoFer4der4fZbAZgS/T+4IMPcOzYMfz5559Yt24dpkyZgueee04KjJ599lmoVCqMGjUKJ0+exMaNG/Hhhx/KhtQmTZqExMRE/POf/8Tp06eRkJCAQ4cOYcKECQAAhUKByZMn4+2338bXX3+NEydOYNiwYYiMjMSAAQOq4r0jIvI4B1KzkZ1fJD0vvR7dQFMCzlvDZLWbAGDOthRYGkYCvhp4KRWIaRWCx7s0QUyrEAZORKW4VaogLy8P8fHx2Lp1KzIzMxEZGYkhQ4Zg1qxZUKlUSEtLQ1RUlNNj9+7di/vvvx9HjhzBuHHjcPr0aZhMJkRFReH555/H1KlTZflOx48fx/jx43Hw4EGEhoZi4sSJmDZtmuycmzdvxsyZM5GWloY2bdpg4cKFePjhh6X9QgjMnj0bK1asQG5uLnr16oWlS5eibdu2Lt0vSxUQkccqNNgqhmuayDZvPZKOBZv2IB9+CMA1bCy1kK8OIQ4B1TPXF/hlCQKqK6r7+9ut4Km+YfBERB6pnPpNG3b9gpj/DUcWAjHe/Ao+Vi1GCIxS4GRnD6CyEIjh5unIgz8+HNwFj3dp4uwViWqV6v7+5sLARES1TYk16go/6Ycf716NwPAo9GhUgEd+ewkNlJmAFRBQYLh5OgJwDXrIe5R0CHGYURfW0Lcm7oao1uHCwEREtY2mCfZEr8ZFhMP36gW0TxyCRf/+D3Qf9kaDgovSEJ19xlzpwMnOvl8BIELjy+KXRC5i8EREVMvM25mCkV/q8HThTCn5e4s6AU2RIcttcoU9FXz2ox2YGE7kIgZPRESersTivtuPXpJqOOkQgreL5Cs8TCka53LgBABaja+tTEHHiKq7XqI6jjlPRESerERy+J7o1Zj4pU7a1RnnsFT1oaz5+z5LK+x5iu9/O0IbqlnDiaiS2PNEROTJSiSHt9r5jFSXqTPO4XN1grSA72jTFFn9pojr7ZwJbahmDSeim8DgiYjIg1kaRuLg/f/FRYRLgdFDioOywOlJUwJ2ib9JC/qWLoBZGmfVEd0cBk9ERJ7meo5TYrIOvRbswVPrL8qSwz9Rvy8LnI6hNQBbDpQ9gMpCIPLhJzstZ9URVQ3mPBEReZLrOU75OXrMyXpDyl2yJ4d/on5fajrOPEkKnOyc1W8COKuOqCqx54mIyJOYriI/R4+A/Iuy3CVnyeEzfdY5zW3SIwT5Cn/ZNs6qI6o67HkiIvIgiReVmJP1hrT23AbVXLxTNBQfqz6UhurGmSdhps86ab+z2XVWcXOz6ixWgQOp2cjMK+SsPKJSuLZdObi2HRHdSharQK8Fe6AzFMoW77UrmeNU1uK+JVV2rbrEZB3mbEuBzlAobYvQ+GL2ox3Yc0W1QnV/f3PYjojIQxxIzZYCFh1CMKVonGz/+BI5ThUlhwOVm1WXmKzD2LVHZIETAOgNhRi79ggSk3VlHElUf3DYjoioJhQabDWcNDd6hjLzbAGLFllogGt432ep7JA3fdbhhPk2WRJ5Wcnh2krMqrNYBeZsS4Gz4Qhx/bxztqXgoQ5aDuFRvcaeJyKiW81eNXzNw4AhXdoc1tAXEcjC56oE7FD/XRqSG2hKKLMApn1xX7ubmVVXsufLGQFAZyjEgdRst85LVNcweCIiupUKDcCVc1LVcKzpLwVQPVR/YqtvApoqs6BWFCPdGorB5ngcEW0dCmDeGXgVL98XhQiNfGjuZmbV2Xu+qqodUV3FYTsiolulxDp1GPRv4IsXbwRQfd6G1+YR0KIYJuGNKyIIT5lny4boBpvjsSNwHhoFhmPryDh4+Qfhjb63V9msOFdzpFihnOo7Bk9ERLdKiXXq8MWL8gBq43O2NkpvHL3n33hnvxk6cwPpUNtst4fQqFlvQN0A8NUAALyUCsS0KnsRYHf0iGqECI0v9IZCp3lPlc2lIqprWKqgHCxVQERVzpBu62nKSQOCWwJ3TwJ2TLmx/5m1wO2P1lidJftsOwCyAMr+yiy0SbVBdX9/M3gqB4MnIqoWJQOo0oJbAiN2AJqmt/qqJKzzRLUdg6caxOCJiKpNyjZg03M3nvd/H/jlwxs9UjUcQLHCONVmDJ5qEIMnIqqU3IvA1UygaXfHfemHgat6YNMwwFp8Y3twS3kOVHBLYMROWR0oInINK4wTEdUmuReBpdHAqj5A+kH5vvSDwMqHgA3P2gInpTfw9FpboFQyiTy4JRDQ2JYYTkQeh8ETEVFVupoJFJtswdGqvjcCqPSDwMo4QFgAAKJBOI732YSvzN1w+IG1ELIAahXw3BfSjDoi8iwsVUBEVEUsVoEDppYw9/ov7v3f81DaA6h+i4BvXpcCp0LfcDxjnotjXxYCOAoA6Bz4d6wPmAv/gMZAaGsGTtcx94o8EXOeysGcJyJyVekZap1xDl/4zoE3LDcaKb1x4J5PMGnXVan4pZ0CtjXt5j7TE7Fd297CK/dcnPVHlcWcJyIiD2evjVTyS/4YWiPePFzW7sSdMzHpgMYhcAJsNZX0CEF84kVYrPyb1tl7CgB6QyHGrj2CxGRdDV0ZEYMnIqKbYrEKzNmW4lCRuzPO4S2fNbJt7X97C2GG5DLPxYV3bcp6T4EbhTvnbEthkEk1hsETEZG7Cg2A4RIA4EBqtqx3RIssxCAZn6sT4KOwokgoMcM8EkVCCR+FFZ+rE9AZ58o9fX1feLf0e1oag0yqaQyeiIjcYV/cd83DgCFdFuhEIAtbVfH4TP2uFDg9aUrAemssnjQlyAKoTuUEUPV94V1Xg8f6HmRSzWHwRETkDtNViOuL++b/qy+OnzwJwBY4bVDNRYQyFwJA8fXA6RhaA7DlQNkDKBN8cAVBDqdWwJYQXd8X3nU1eKzvQSbVHJYqICJyQ+JFJZbl/x2LrTPRouAihp0Zj98U4/C+z1K0UGbivDUMr5jHwwoFTlwPnOyOoTUGmhJwBUHQI1S2zz75fvajHer9VPweUY0QofGF3lDoNO9JAUDLIJNqEHueiIhcZJ8BdszYAIPN8ThvDUMLZSa2qBOkwGmwOR7H0MYhcAJsX/pXNB0R/+xD0GrkvSZajS+WPdeNU/ABeCkVmP1oBwA3gko7BpnkCVjnqRys80REduZiK3rO243sfLO0rZvid2xRJ0jPB5oScESUXaNJAUgBEos/Vox1nqiyuDBwDWLwRESA7Uv871tPIDu/SNpmz3FqocyUttl7npzVcQry98H8gZ34pe8mBplUGSySSURUg+xDdWUFTuetYRhoSpCG8Dao5iICWQ7n+XgIh+Qqw0upQEyrEDzepQliWoUwcCKPwOCJiKgMzoo1aksFToPN8Tgi2spyoDao5kJbIoCK0PiiZyvH3igiqp0YPBERlcFZscZ8+CELgQ5DdDqESAFUFgKRDz8AtjwnJjcT1S0sVUBEVAZnRRjz4I/h5ukIwDXoS+U26RCCZ8zxyIcf8uCPRgE+ePcJ5jkR1TUMnoiIylBWEcY8+CMP/k732QOqkAAVkmb0hsqbHfxEdY1bP9UWiwXx8fGIioqCn58fWrVqhblz56LkhD0hBGbNmoWIiAj4+fkhNjYWZ8+elZ0nOzsbQ4cORWBgIIKCgjBq1ChcvXpV1ub48eO499574evri2bNmmHhwoUO17N582a0b98evr6+6NSpE3bu3Cnb78q1EBGVxV6s0Z0BN8X1xztPdGTgRFRHufWTvWDBAixbtgxLlizBqVOnsGDBAixcuBAfffSR1GbhwoVYvHgxli9fjv379yMgIABxcXEoLLzR/T106FCcPHkSu3btwvbt2/HTTz/hpZdekvYbjUb06dMHLVq0wOHDh7Fo0SIkJCRgxYoVUptffvkFQ4YMwahRo/Dbb79hwIABGDBgAJKTk926FiKisrhSrDHI30e2ncUuieoB4Yb+/fuLkSNHyrYNHDhQDB06VAghhNVqFVqtVixatEjan5ubK9RqtVi/fr0QQoiUlBQBQBw8eFBq88033wiFQiEuXbokhBBi6dKlIjg4WJhMJqnNtGnTRLt27aTnTz/9tOjfv7/sWqKjo8XLL7/s8rVUxGAwCADCYDC41J6I6qZvTlwWPd/9XrSYtl169Hz3e/HNicui2GIVv5y7Ir78LV38cu6KKLZYa/pyieq96v7+dqvn6e6778bu3bvx+++/AwCOHTuGffv2oV+/fgCA1NRU6PV6xMbGSsdoNBpER0cjKSkJAJCUlISgoCDcddddUpvY2FgolUrs379fanPfffdBpVJJbeLi4nDmzBnk5ORIbUq+jr2N/XVcuZbSTCYTjEaj7EFEdUyhATBccr7PcAkoNMBiFUj6IwtfHb2EpD+y8FAHLfZNexDrR/fEh4O7YP3ontg37UH07RjBOkRE9ZBbCePTp0+H0WhE+/bt4eXlBYvFgnfeeQdDhw4FAOj1egBAeHi47Ljw8HBpn16vR1hYmPwivL3RqFEjWZuoqCiHc9j3BQcHQ6/XV/g6FV1LafPmzcOcOXNceCeIqFYqNABrBwH5fwEjdgCaptIuS85FFK18GFdEQzx77XVcKLjxxxuXBCGiktzqedq0aRPWrVuHzz77DEeOHMGnn36Kf/zjH/j000+r6/puqRkzZsBgMEiPixcv1vQlEVFVKTQAV87ZAqecNGBNf8CQDgD45cdvkfHhg/C9egGWvL9gLsiTHao3FGLs2iNITNbVwIUTkadxq+fp9ddfx/Tp0zF48GAAQKdOnXD+/HnMmzcPw4cPh1arBQBkZGQgIuLGX2gZGRno0qULAECr1SIzM1N23uLiYmRnZ0vHa7VaZGRkyNrYn1fUpuT+iq6lNLVaDbVa7dqbQUS1R8kep0H/hvjiRShy0pD/r77Y03wi+p6aDh+FFRetjTHYHO9Qv8k+n3jOthQ81EHLoTmies6tnqeCggIolfJDvLy8YLVaAQBRUVHQarXYvXu3tN9oNGL//v2IiYkBAMTExCA3NxeHDx+W2uzZswdWqxXR0dFSm59++glFRTfWktq1axfatWuH4OBgqU3J17G3sb+OK9dCRPWE6arU41Sw/gW8eHUMzlvDEFBwEY+efgM+CiuKhBLjzROdLuprpzMU4kBq9i28cCLyRG4FT48++ijeeecd7NixA2lpadi6dSvee+89PPHEEwAAhUKByZMn4+2338bXX3+NEydOYNiwYYiMjMSAAQMAALfffjv69u2L0aNH48CBA/j5558xYcIEDB48GJGRkQCAZ599FiqVCqNGjcLJkyexceNGfPjhh5g6dap0LZMmTUJiYiL++c9/4vTp00hISMChQ4cwYcIEl6+FiOoJTRNgxA4UBDSDf/5FzDK9h+XFj8iajDdPwnG0rvBUu1Kc50wSUf2hEEKIipvZ5OXlIT4+Hlu3bkVmZiYiIyMxZMgQzJo1S5oZJ4TA7NmzsWLFCuTm5qJXr15YunQp2rZtK50nOzsbEyZMwLZt26BUKjFo0CAsXrwYDRo0kNocP34c48ePx8GDBxEaGoqJEydi2rRpsuvZvHkzZs6cibS0NLRp0wYLFy7Eww8/LO135VrKYzQaodFoYDAYEBgY6OrbREQ1rdBg623SNJE2WawCA+dvwseFM9BUmeVwSOm16soSEqDCgTdjOXRH5MGq+/vbreCpvmHwRFQLlTGjLumPLEz9ZAe+Us9EmMIgNZ9hHokx3tvRQpnpcgC1fnRPxLQqvw0R1Zzq/v7m2gFEVLeUyG/Cmv6w5FxE0h9Z+PnIUWxRzZIFTgAwxns7XjFPwHlrGFooM7FBNRdaOPZMleRswWAiqj8YPBFR3XI9vwnBLYGcNOg+7I1F//4PBp94CRFKW5HdIqHAaNMUKWBarFoiBVBZCEQ+/Mp9ibIWDCai+oHDduXgsB1RLeAkvwkAfjhwBK23P4Wmyiuy7ZeswRhrnoLjaI0IZGGDaq40ZDfBPAFpiEQe/J2+lAK2tev2TXuQOU9EHozDdkREZbHnN615WCp4CdiSw9///iy8YZE111uD8aT5LWlWnQ4hGGyOl3qcHr7/Pjx9TwenL2UPlWY/2oGBE1E9x+CJiGqvUvlN9gDqaHIyPi6cAe31YTq7Yng5nEKHEEz0fRvZT6zH2L5dEf/oHVj+XDdEaORDc1qNL5Y9141LtBARh+3Kw2E7olrAkG4LnHLSbHlOT6xAwWfD4V9oW0ol3RqKV4om4H2fpQ4z6obFtEC/jhHoEdXIoTfJYhU4kJqNzLxChDX0ddqGiDwTSxXUIAZPRLVEyQCqhHRrKJ4yz4YOIQ75Tc+Y4/H+6P4sOUAegcF61aru72+31rYjIvJImqbAEyuAVX2kTX+hEZ6+HjgBN/KbNqjmIguBaBAYhB5RjWrqiokkick6zNmWAp3hRgmMCI0vZj/agcPEHoo5T0RUOxQaAMMl5/vSDwFbXpRtauBnW+S75N/u9gBqhHk6Xnvsb/zLnmpcYrIOY9cekQVOAKA3FGLs2iNITNbV0JVReRg8EZHnK2NWHQAg/SCwKg7IvQAENQdGfgcEt4TfNR2+D1mIOwOvyttrmmDhc734Fz3VOItVYM62FDjLnbFvm7MtBRYrs2s8DYftiMjzlZ5VZ192Jf0QsKovYC0GlN7Ak6uBpnfZ9q/pD/+cNHwZ/C6OPLIO6dZg5pKQRzmQmu3Q41SSAKAzFOJAajZz8zwMe56IyPNpmsAybDuuNWgO5KQhd2kfHP35W1xbPxywFsOq8IZ52DdIMkXhq6OXkHTFD5Zh24HgllAENEb3ts3weJcmiGkVwsCJPIary/xwOSDPw54nIvJ4ick6TN9yCn4Fr9tmzJkuocuupwEAF6yhmGB+Bcn/+gtW8Zd0TITGF+/2XoMH7rwN8NXU1KXXSpz5dWu4uswPlwPyPAyeiMijJSbrMGbtEQBALkIwpWgctqgTpP2TiybYKoaXSgvRGwoxcstlLPPXom/HioMnBgw2nPl16/SIaoQIjS/0hkKneU/25YA4K9TzcNiOiDyWxSqQ8PVJ6XkEsvC+z1JZm/d9liICWQ7HupNwm5isQ68FezDkk18xacNRDPnkV/RasKfezXTizK9by0upwOxHbcsBlQ7TuRyQZ2PwREQe60BqNvRGEwA4FLkcaErAeWsYWigzsUE1t8wAyp5wWxYGDDac+VUz+naMwLLnukHL5YBqFQ7bEZHHKD10pjdcAwBoSwVO9uVV7EUv7QHUM+Z46OE4K6mshNuKAgYFbAHDQx20df6vf878qjl9O0bgoQ5aDhvXIgyeiMgjOMu1Cfb3AQDkww9ZCASskAInwLFqeD78nJ67rIRbBgw3cOZXzfJSKur8/2N1CYMnIqpxJZPCS8opKAIA5MEfw83TEYBrDj1LOoTgGXM88uGHPPjL9lWUcMuA4QbO/CJyHXOeiOjWK7HUisUqMH3LCWmXFlloiAKHQ/Lg73RIDgD0CHEaOAHlJ9wyYLjBPvOrrIEiBWyz7jjzi4jBExHdaqWWWlmy5xxyr/cwRSALG1Vz8alqvtMAqjylv/RdSbhlwHADZ34RuY7DdkR06xQagCvnpKVWrn3SD18ZpwEIxp04h49VH6GZ8i/ACgTgmkNvUlleebA1JjzYBofP57iVcGsPGMauPQIF5KWi6mPAYJ/5VTr3TMs6T0QyCiEE552WwWg0QqPRwGAwIDAwsKYvh6h2u97jJK7+hcVB0/BEagKaKzJw3hqGd4qG4mPVh/BRWHHR2hhPm2dJSeEVGX1vS7zZ/46bujQWhpRjwVCq7ar7+5vBUzkYPBFVkdyLQMZJ5GyZimDTJZy3huEV8wQsVi1BC2Wm1KxIKDHIlGCrGF6C2kuBIqtAyRJDSgUw+t4ozHi4Q5VcIgMGorqjur+/OWxHRNWn0ABc+g3YMASWIhNeK5yMWar/ooUyEx+rFuOn4o5ooboRPI03T3IInABg3AOtMfb+1vhvUhrOZxegRSN/PB/TEirvqkvb5FRxInIVe57KwZ4noptQaAD+MwDQn4CwFkEBW8/SGNMUzFF9iqbKKw6HlCyAaRfs74NDMx9iLxARuay6v785246Iqofpqi0x/HrgJATgo7Biufo9aBR5sqZrzLFOl1pRAJg3sBMDJyLyKAyeiKhqXa/hZA6IwHj1u0i3hgIAFAp7ACXQUGGSHfKA93G8Yp4gC6Da+hm5thcReSQGT0RUdTJSgJVxyFn6EO6fuRY7LnjhKfNsWQBV0hdF90gB02LVEimAykIg3nqyJwMnIvJIDJ6IqGpcOACseADir1MINl3C+uvDbzqE4LuiLk4Pecw7CW+Zn5cFUBPME/CG72z87faWt/TyiYhcxdl2RHTzMlKA/zwCYTFJ+U324bdfi9riadU+h0Nu5EC9jzGmKZil+i+yEIjziMTCx/7mkOfEUgJE5CkYPBFR5RUagDPfQuyKB4pMUl6T/b8tlJloob5RikAIIBcBCFbkl8iBsgVQo01TcLFhZyx8uofDcB2LWBKRJ2HwRESVk5EC/PcJiKt6oETAVPK/JQkBvGh6FSloic2qOWiqvCK1LYIP7u31AEY83MuhNykxWYexa4+gdE0VvaEQY9ceYVI5Ed1yzHkiIvcdXAMsi4HI00OBigMnAPhLaJCCltAhREoiNwlv/C4i8ZT1HaeBk8UqMGdbikPgBNxYh27OthRYrCxXR0S3DnueiMg9K2KBywcdgqTyAichgDClARtUc6UimE+aZyMA15CJRsiDPw6kZjtU+D6Qmi0bqnM4LwCdodDpsURE1YU9T0TkundbOA2c7MoKnErmQG1QzYUWWdAjBH+gKfLgDwDIzHMMkpxtc8bVdkREVYHBExG5ZtHtgDm3zMDJmdJDeUIABVAhH34ObcMa+rq0zRlX2xERVQUO29WgAnNxmfuUCgV8fbyqve01swXCaUYJoIACfqrKtS0sssBazrKJ/irvGm/r5+MFxfUowFRsKTdvxp22vt5eUF7P3TEXW1FstVZJW7W3l5QT5E7bIosVRZay26q8lPD2UpbfNnEWcDULPkIJH6Vtf7FQwgyfMs/rLYqhUlogBGCBEiarDwrhjfHmV6TeJjttoBo9ohoBsOU5mYotAICOTQIRHqhGhtHkcH678EA1OjYJdPr/vbdSKS0ebLUKFF4/r9PrdaOtl1IBtbft/3chBK4VVU3bW/Vzz98RztuW3EdUHv6fUoM6zPq2zH0PtGuM1S/0kJ53n/t9mb90o6MaYePLMdLzXgv2Ijvf7LTtnU01+HpCL+l57Hs/4lLuNadt24Q1wK6p/yc9f2zJPpzNvOq0bZMgP/w8/UHp+dP/SsLxdIPTto0CVDgS/5D0fPiqA9ifmu20rZ+PF07N7Ss9H7v2MPae+ctpWwBIm99f+vfUTUex84S+zLYpb8VJvyz/viUZXxxJL7Pt4ZmxCGmgBgC8vf0U/vvr+TLb/u+NB9CskS04+Md3Z7Dipz/LbPvdlPvQNrwhAODjvefw4e6zZbb9avw96NwsCACw+udUzPvmdJlt14/uKeUArT9wAbO+Ollm21Uj7sKD7cMBAF/+dgmvf37cSat7ANyDj30+RH/sBwB8a/0bxhdNKvO8i7yX40nFT1AogB+L78So4jfKbHt/uzAp2DuQmo0hn/xaZls7BWw5TxlGEzolfOe0zaTebTDlobYAgHN/XUWf938q83wv3Xcb/v7w7QCAS7nXcO/CvWW2fb5nC8wd0BEAkJ1vRve3vy+z7aBuTfHPpzsDAK4VWcr9uX+4kxZLh3aXnvN3hM2t+h1Rch9ReThsR0QuK+cPeqfsQ3XZaFBuuw6R7q96rtX4Ynrfdm4fR0R0sxRCuP7rsGXLljh/3vEv7nHjxuH1119HVFSU0+M2bdqEp556yvaCTpIl1q9fj8GDB0vPf/jhB0ydOhUnT55Es2bNMHPmTIwYMUJ2zMcff4xFixZBr9ejc+fO+Oijj9Cjx42/wgoLC/Hqq69iw4YNMJlMiIuLw9KlSxEeHu7q7cJoNEKj0cBgMCAw0P1f7hVhl3zNtuWwnY3TYbtCI5Cnx8xvUrHlHBCOLHzqswCtlZfhBSsUioqH7XxQDG/YekKGmKbjV9wp2x/s7429rz0AlbcSPl62ByAftivJYhU4lJaD7HwTIoP8pWE+Z23tOGx3823r0+8IDtvVHdX9/e1W8PTXX3/BYrnxg5+cnIyHHnoIe/fuxb333ou//pJ3la5YsQKLFi2CTqdDgwa2vzwVCgVWr16Nvn1vdLMGBQXB19eW8JmamoqOHTtizJgxePHFF7F7925MnjwZO3bsQFxcHABg48aNGDZsGJYvX47o6Gh88MEH2Lx5M86cOYOwsDAAwNixY7Fjxw6sWbMGGo0GEyZMgFKpxM8//+zym1Pdbz6RRyo0AP95HBb9SeiKA/GUeTZ0CEEEsqTilq6w/2Z5zTQSXyBWtk8BsLglEVUbjwqeSps8eTK2b9+Os2fPOu1R6tq1K7p164aVK1feeEGFAlu3bsWAAQOcnnPatGnYsWMHkpOTpW2DBw9Gbm4uEhMTAQDR0dH429/+hiVLlgAArFYrmjVrhokTJ2L69OkwGAxo3LgxPvvsMzz55JMAgNOnT+P2229HUlISevbs6dL9MXiieslwCQXLe8P/mg4AkG4NxVPm2QCAfaqJ8HJhsN/+W2WCaSx24F7ZvoqWVeEadkR0s6r7+7vSOU9msxlr167FyJEjnQZOhw8fxtGjRzFq1CiHfePHj0doaCh69OiBVatWoWT8lpSUhNhY+V+pcXFxSEpKkl738OHDsjZKpRKxsbFSm8OHD6OoqEjWpn379mjevLnUxhmTyQSj0Sh7ENU3O88r0Tvn70i32hLOmyqvYKt6Fn5yEjg5+9OrZI9T6cApvv/t2DftwTIDp8RkHXot2IMhn/yKSRuOYsgnv6LXgj1ITNbd9H0REVWVSgdPX375JXJzcx1ykexWrlyJ22+/HXfffbds+1tvvYVNmzZh165dGDRoEMaNG4ePPvpI2q/X6x3yksLDw2E0GnHt2jVcuXIFFovFaRu9Xi+dQ6VSISgoqMw2zsybNw8ajUZ6NGvWrKK3gahO+fpIOsZ9duT6EioJUgClVeTAp8RvCyGAQquXlBBecjsALDQNcBiqC1B5YcQ9UWX2ItnXsCtdUdy+hh0DKCLyFJUOnlauXIl+/fohMjLSYd+1a9fw2WefOe11io+Pxz333IOuXbti2rRpeOONN7Bo0aLKXkaVmjFjBgwGg/S4ePFiTV8S0S3z4qcH8MqmY9JzHULwStFEh3ZCAJ+Z7sMD5g9w3homBVC2ek62HqdleNqt1+YadkRUm1QqeDp//jy+//57vPjii073f/755ygoKMCwYcMqPFd0dDTS09NhMtmK4Gm1WmRkZMjaZGRkIDAwEH5+fggNDYWXl5fTNlqtVjqH2WxGbm5umW2cUavVCAwMlD2I6rRCA2C4hFFrDuL7U/IJH3fiHD7y+dDhkEyhwRI8BR1CMNgcj/PWMFwVvthsisa9psUOPU52+WYLDpRRq8edNeyIiGpapYKn1atXIywsDP37Oy8otnLlSjz22GNo3Lhxhec6evQogoODoVbbChDGxMRg9+7dsja7du1CTIytwJtKpUL37t1lbaxWK3bv3i216d69O3x8fGRtzpw5gwsXLkhtiOq9QgOwdhCyl8Yi5fQp2a7OOIct6gQ0UeYAADJEEDKttj8mwpUGbFbNQQSyoEMInjHH427zEryBSdAhtNyXLGsNOq5hR0S1idtFLaxWK1avXo3hw4fD29vx8HPnzuGnn37Czp07HfZt27YNGRkZ6NmzJ3x9fbFr1y68++67eO2116Q2Y8aMwZIlS/DGG29g5MiR2LNnDzZt2oQdO3ZIbaZOnYrhw4fjrrvuQo8ePfDBBx8gPz8fL7zwAgBAo9Fg1KhRmDp1Kho1aoTAwEBMnDgRMTExLs+0I6rzTFeRn6NHI9NlbFDNxWBzPHQIwZ04hy/UCfBW2GpD6azBGGh+CwCwWZWApsosNFVewWbVHDxpng09Qlx+ybLWoOMadkRUm7gdPH3//fe4cOECRo4c6XT/qlWr0LRpU/Tp08dhn4+PDz7++GNMmTIFQgi0bt0a7733HkaPHi21iYqKwo4dOzBlyhR8+OGHaNq0Kf79739LNZ4A4JlnnsFff/2FWbNmQa/Xo0uXLkhMTJQlkb///vtQKpUYNGiQrEgmUb1WaABMVwFNE1gaRuJhwzT8R/kWWigzsVk1B9OLXsQ8n5XwVlghBKAXtsBJdz1AesqcgM2qBDRWGJCFQKcL/JYl2N9HKmxZWo+oRojQ+EJvKHSa96SAraJ4WccTEd1KN1Xnqa5jnSeqU64P0yH/L1iGbcdzm9KRlJrttPjlRWtjvG4ejTREOPQsaZGFBriGDDRyWOC3PKPuaYn4R+8oc799th0AWQBln5vHoppE5CqPrfNERLWM6SqQ/xeQkwbdh72RlnpjEWL7Mip2k4rG41d0dDokp0cIzqGpW4ETAMR2KHuyBgD07RiBZc91g1YjH5rTanzLDJwsVoGkP7Lw1dFLSPoji7PxiOiWYM9TOdjzRHXNnl8Po9XOwWihzMR5aximFI3DYp+P0FSZJWt33hom5UBVhQiNL/ZNe9ClSuGuVhhPTNZhzrYU2Sy9iqqXE1H9wJ4nIqoSO4/r8OJXeqm8QAtlJraoE6TAKd0aioGmBGnfBtVcRCCrgrO65rHOEU4DIGc9R15KBWJaheDxLk0Q0yqkzMCJBTWJqKaw56kc7HmiWqtEYjhgCzbGXM8n0iILrRWXsFY9X2quF8F4wvSWtADwBtVcqXfqGXO8WzPqyjIlti0mPNhaCoYq23NksQr0WrCnzLpQ9uRyV3u6iKjuYc8TEbnnemK4WPMwDh8/ga1H0vH3rbaFtiOQhc9VCVip+ofskGJx41dByeKX7s6oK8/73/+Oe+bvRmKy7qZ6jlhQk4hqGoMnorrGdBUFOXooctIQ+vlALNy0B9n55uuz6mzDdGpFMdKtoXjONB3p1hA0VWbJhunsxS+Hm6e7nRheHr3RhDFrj2D6lhOVXoqFBTWJqKYxeCKqYxIvKhGb9YYsd6mb4ncpcAJs+U1PmWdjn7gTT5nleU7a6wGUHiFVGjiVlFtQVOa+inqOqqugJmfuEZGr3C6SSUSey1xsxYwtJ5BzfejNnru0RZ0AADAJb/wlgvCUebY0k05Xom1VDtPdrLJ6jqqjoCZn7hGRO9jzRFRHJCbr0G3uLuRc79XRIQRTisbJ2owyv4YnSwROdtU1THczyuo58lIqMPvRDgBuFNC0sz+f/WgHl5PFOXOPiNzF4ImoDrDPprtqKpa2RSAL7/vIlyR6x2eVQ8BhV53DdO5QwNbrU17PUWUKajpjsQrM2ZZS6fwrIqqfOGxHVMtZrAIJX6fItpUuNzClaBze91kq5TVVZQFMdykAaPx9YLjeQ+ZsKRZXeo76dozAQx20LhXULIs7M/diWtXM+0VEnoc9T0S13IHUbOiNNwIAbanAabA5HkdEW1lxzJKJ4beSPayZP7BTlfQcuVJQszycuUdElcGeJ6LaJPcicDUTaNpdWsbkXz+eAwB0wjlcQRCuwh9ZCASskPUweUJiuLZUEvbN9hzdrOqauUdEdRuDJ6LaIvcisDQaKDYh6f/WYeovPtKQU2ecw+fqBJjgg4dMizDcPB0BuOZQGdyeGJ4Pv1ua3/REl0g8/bfmDsGRveeoplTHzD0iqvs4bEdUW1zNBIpNgLUYd+0ZgjCDrWq4PXDyUVihRhFCkYs8+Je5pEpNJIY39POp1LBadavqmXtEVD8weCKqLZp2h2XENyiGF3wUVnyuTsAQ5fdS4FQklHjSlIATaF3TV+qgRaOan8VXlqqauUdE9QcXBi4HFwYmT2HPb/r53BX8b2+iFDDZ2QOnYx4YOCkAnHm7H1Tenv23mv09rqn8KyKqOtX9/c2cJyIP51j9ujVmFY3APNUqqc2sohE3FTgpAKc5P1XhkTu1Hh84ATWff0VEtYfn/0Yjqsd2Hr+MMaWqX3fGObzls0bW7i2fNeiMc5V+Ha3GF8uf64blz3VDRKnhq5AAFfp30lb63JypRkR1DXueiDzUzuM6TFj/m2xbyeTwIqHErKIReMtnjZQD5e7Q3ah7WiK2g1Y2ROWsfAAAfHvyGxRXotL2yp/T8LeoRswdIqI6g8ETkYexWAWW7DmH97//Xba9U6nAyR4opZhaSts/VydgoCkBVzQdEd//dgQHqPF9ih5bj15Cdn6RdK7yFr11NnxlsQrcTPrPnG0peKiDljlERFQnMHgi8iCJyTokfH0SeqPJYd8VBMEEH0AUyXqYjqE1njQlSHWenu19F57ufbcUqMS0CsHf+3e46WVMzJbKZ0VxiRMiqksYPBF5iMRkHcauPVJm4rYOoXjItAihyHUoR3AMrTHQlABlw3BsKRE42d1sMnRVLE/CJU6IqK5g8ETkASxWgTnbUiqc8aZDKHQIddiuAJCM1lj2eLdqGRqriqRvJo4TUV3B2XZEHuBAarZsRp27qrugY4+oRvD1qfyviwgucUJEdQh7nohqQqEBMF0FNE0AyIe0tMhyee05hQL4z4geuLtNaLUmY3spFbi/bWMknsyo1PFc4oSI6hL2PBHdaoUGiP8OQuEnffHtL4eQ9EcWQgPUAIAIZGGjai4+Vc1HQxRUeCohAG9v5S0JTIZGt3D7GIUCWPoslzghorqFPU9Et9je43+izaULaIoMtE8cgsHmeIjAJmjnZ8AKy1y0UGYCViAA11zqfbpVidjKSgRogb7eiOtY+QKbRESeiD1PRLdQYrIOI7dcxlOFM3HeGoYWykxsUM1FZN5xrLDMRgtlJs5bwzDYHA89XJsdd6sSsa9cdSyfUBHDtWIcSM2uhqshIqo5DJ6IbpGSM+p0CMFgc7wUQG1RJ8gCJ50LgZMCtzYRu7JBGksUEFFdw+CJ6BYpPaNOhxBMKRonazOlaJxLgRNgW8h38N+aV+Ullqt7i2AoKpFaxRIFRFTXMHgiukW+T9HLnkcgC+/7LJVte99nKSKQ5fI53//+d/RasAeJyboqucbyHD6fA+FmkXGWKCCiuojBE9EtYLEKbD16SXoegSxsUM2VhuoGmhJkOVDuBFB6QyHGrj1SrQGUxSrw87krbh/HEgVEVBcxeCKqaoUGwHBJtulAajay84ugRRZaIV0WOA02x+OIaCvLgdqgmgutiwGUvTNozrYUWKyVX3+uLInJOvRasAdL9p5z+RhbiYKuLFFARHUSgyeiqlRoANYOAtY8DBjSYbEKJP2RhW+SdVINp/dUy5GDAIfk8JJJ5FkIRD78XH5ZgRuL71Yl+3p77lY/Xzy4Kx6+M7JKr4WIyFOwzhNRVTJdBfL/AnLSULCiL4YUxeOYsYFsmA5WYJT5NVyFn0M5Ah1C8Iw53uUK46VV5cw2V9fbK+2ROyPwaGcGTkRUd7HniehmlRym0zQBRuxAQUAz+OdfxMeFM9BLcdxhmO4cmpZZx0mPkEoFTkDVzmyr7Hp7D3UIr7JrICLyRAyeiG5GqWE6ALA0bIIhRfFIt4agqTILa9Xz3a7h5K7qqPlU2V4sliYgorqOwRPRzSgxTIc1/QFDOg6kZiPTaIK3wipr6k4NJ3fY57JV9cy2ygZBOfnmKruGithzyr46eglJf2RVS8I8EVFpbgVPLVu2hEKhcHiMHz8eAHD//fc77BszZozsHBcuXED//v3h7++PsLAwvP766yguLpa1+eGHH9CtWzeo1Wq0bt0aa9ascbiWjz/+GC1btoSvry+io6Nx4MAB2f7CwkKMHz8eISEhaNCgAQYNGoSMjMqtCE9UpuvDdAhuKQVQRWlJ2KyaA60iR9bU3RpOZQnyk6cqajW+WPZc1S++2yOqESI0vnA3HJu7o3pm/ZVmnwU45JNfMWnDUQz55NdbVvOKiOo3t4KngwcPQqfTSY9du3YBAJ566impzejRo2VtFi5cKO2zWCzo378/zGYzfvnlF3z66adYs2YNZs2aJbVJTU1F//798cADD+Do0aOYPHkyXnzxRXz77bdSm40bN2Lq1KmYPXs2jhw5gs6dOyMuLg6ZmZlSmylTpmDbtm3YvHkzfvzxR1y+fBkDBw50/x0iKq10KQJNU1kAdd//hqKp0lYTKd0a4nINpyB/H0zu3abcYOXl+6JwOL4P1o/uiQ8Hd8H60T2xb9qD1VISwEupwOxHO7h9XHXM+iutrFmAt6LmFRGRQgh3awbfMHnyZGzfvh1nz56FQqHA/fffjy5duuCDDz5w2v6bb77BI488gsuXLyM83JZUunz5ckybNg1//fUXVCoVpk2bhh07diA5OVk6bvDgwcjNzUViYiIAIDo6Gn/729+wZMkSAIDVakWzZs0wceJETJ8+HQaDAY0bN8Znn32GJ598EgBw+vRp3H777UhKSkLPnj1duj+j0QiNRgODwYDAwMDKvk1Ul9hznPL/AkbsgKVhE9swXV4h2uX8iPY/jpWa6q3BeML8FnQIcSiK+YyThX+XPmub3p+YrMOcbSmywKBRgA/efrxjjUz/T0zW4e9bTyA7v8jlYz4c3AWPd2lSLddjsQr0WrCnzGR2BWy9cfumPcgCnUT1VHV/f1c658lsNmPt2rUYOXIkFCUWvFq3bh1CQ0PRsWNHzJgxAwUFBdK+pKQkdOrUSQqcACAuLg5GoxEnT56U2sTGxspeKy4uDklJSdLrHj58WNZGqVQiNjZWanP48GEUFRXJ2rRv3x7NmzeX2hBVSqlSBAPnb8KQT37Fqg2fo9UP42VNLfCSepFcqeEUHKAGAPTtGIF90x6U9S4dfPOhGqub1LdjBH6dEYsGatcrm1Rn0nhFswCrq+YVEZFdpes8ffnll8jNzcWIESOkbc8++yxatGiByMhIHD9+HNOmTcOZM2ewZcsWAIBer5cFTgCk53q9vtw2RqMR165dQ05ODiwWi9M2p0+fls6hUqkQFBTk0Mb+Os6YTCaYTCbpudFodOGdoHrFXopgRV/451/EYutMvK0YiqWqD+GjsKJIKPGaeQzmB3+JJgWXscn3bTxVOBM6hFRYw6nk7DYvpQIxrao+ubyyvJQKeHu51otT3evZuToLsCprXhERlVTp4GnlypXo168fIiNv/DX80ksvSf/u1KkTIiIi0Lt3b/zxxx9o1arVzV3pLTBv3jzMmTOnpi+DPJDFKqThudAGvlhgnomPrPFooczEJ+r3AQBFQoknTQk4jtZIK+6CL4PfRdOcNHwbvAB9cqZBj5AyazsBQOj1nidPdCA1G7kFrg3bVfd6dq72arFkAhFVl0oN250/fx7ff/89XnzxxXLbRUdHAwDOnbOtiaXVah1mvNmfa7XactsEBgbCz88PoaGh8PLyctqm5DnMZjNyc3PLbOPMjBkzYDAYpMfFixfLvT+q4zJSgDOJDrO6hv57Pxrl/Y73igbJmo8zT8IxtIYAcMzYAEceWAsEt0TDRhFoEBhU4ay1Vzcf89hEZ1d7cUbd07La17OraBZgddS8IiIqqVLB0+rVqxEWFob+/fuX2+7o0aMAgIgI2y/TmJgYnDhxQjYrbteuXQgMDESHDh2kNrt375adZ9euXYiJiQEAqFQqdO/eXdbGarVi9+7dUpvu3bvDx8dH1ubMmTO4cOGC1MYZtVqNwMBA2YPqqYwUYPk9EOufwedrP5Hl2PTGYaxW/xMfqJbJDpnps042ky7d2ggYsROK57/Aa4/9DQDKDaAyjJ47U8zVXpzYDmX/cVJVSs4CLP1+VlfNKyKiktwOnqxWK1avXo3hw4fD2/vGqN8ff/yBuXPn4vDhw0hLS8PXX3+NYcOG4b777sOdd94JAOjTpw86dOiA559/HseOHcO3336LmTNnYvz48VCrbUMWY8aMwZ9//ok33ngDp0+fxtKlS7Fp0yZMmTJFeq2pU6fik08+waeffopTp05h7NixyM/PxwsvvAAA0Gg0GDVqFKZOnYq9e/fi8OHDeOGFFxATE+PyTDuqxwoNwOXfIIQVCgCfqP+J3jgMABiEvfi3+p9QKACFAsiwasosRRDW0NeWI+WrQd+OEVj2XDeEB5Y9NGef9jpn262pk+QOT+vtsb+fWo08qKuumldERCW5Xargu+++Q1xcHM6cOYO2bdtK2y9evIjnnnsOycnJyM/PR7NmzfDEE09g5syZsh6c8+fPY+zYsfjhhx8QEBCA4cOHY/78+bJA7IcffsCUKVOQkpKCpk2bIj4+XpaYDgBLlizBokWLoNfr0aVLFyxevFgaJgRsRTJfffVVrF+/HiaTCXFxcVi6dGm5w3alsVRBPZSRAnwxEii6hrNtX0brX2dAoQCEAHYW34WHvQ/BPrk0w6rBAPPbTksRTPR9G1unP+3Q+/HzuSsY+u/9FV7G+tE9PSphHLhRWwmAbLFg+x1+/Gw3BAeokJlXiLCGtkCqunt/Suai3arXJCLPV93f3zdV56muY/BUj+ReBPTJwBcjIYoKoACQrYrEu8b+WKT+BCWqcUAIIEs0xKPmd2XLrdgDqCwEIvuJ9Yjt2tbhZb46egmTNhyt8HKqs07SzXBWgypC44vHOkfg62M6h+2zH+3AXiAiuuWq+/u70rPtiOqM3IvA0migqBBWIaCELUBqZL6MV1WfQ0CeW/NdcVfMtox0mDlnL0Xg49cQP3Ru4/SlavtMsb4dI/BQBy1+/SMLSX9eAaCAtxL4YPc5h7b2at8cRiOiuobBE9HVTKDYBAgLrEIJKxTwVggIAUQocxya9/H+DZssaU7LDugRAlyzTe13Nuxmzx3SGwrhrMvXXh3bk2eK7UrRO/Q+OWMPOudsS8FDHbQcTiOiOqPSFcaJar1CA5B5GmiohWXENyiGF7wVVgAKFAvIhuoAYG9xJ4jr2/9dIoncmbKm9tf2mWJlrSlXlvpc7dtiFUj6IwtfHb2EpD+yPG4SABFVHoMnqp/O7QWW9QL+dR+wKg5Hc/0wqHA2ioQS3gorvJ3ELrcpM/C6abQsgLq/jACqvGG32jpTzGIVmLMtxWmPWUXqW7Xv0rXBhnzyK3ot2OORZSiIyH0ctqP6J2U7sGnojeeGi7j9m6eQib/j16J2uFd1yuEQIYAWykxMVH2F102jsUj9CaxQIB1hDm1DAlQVDrvZc4dq00yxitaUK4+n5nBVB3vvXOkgkzlgRHUHgyeqX1K2A5uGOWz2v6bDj+rJ8IFFtt0qIMuBsgdQU00v4QRa4RyaOZzr8S6RLgVBnrZ+XUUq03tUG3K4qlJ5vXPMASOqOzhsR/XHhQPXe5zkAZL9i06lsEh5TkIAxUIB2/ebAsVCIdV7KoAK36OH08AJAB66BVW2a4K7vUe1IYerqlXUO1efc8CI6hIGT1R/ZJ112FS6DIHdu+bBGGSaI+VA2QOoa1Bjonki8uDv9CXq8ppqPaIaIcjfx+X2np7DVR1c7Z2rbzlgRHUNh+2obis0AKartmVSug4FrBZg20Rpd1n9IcN9vsd28z140pSAz9UJMMEHE0zjcQYtoUOoQ/v60MuyK0WP3IKicttMiW2LlqH+tSKHqzrURB0vVlknuvUYPFHdVWgA1g4C8v8CRuwANE2B7tfznUoEUCXZZ9I1VV7BZtUcPGWejYGmBFxBkNOgyU5bx6tp23N5yhPs74MJD7au11/ct7qOV1kV3+vy/4tEnoDDdlQ3FRqAy0eBPD2Qkwas6Q8Y0m37wjsACi+HQ+yBk33BInsA9ReCYQqQfxFFaHwxJbYtPhzcBetH98S+aQ/W6S8rV2ba5RQU1ftcnltZx6usmlv2WX0si0BUfdjzRHVP7kXgs8G2HCf/RoCm2Y0AqtdUYNsrDoeUDJzs/y2GElkIRIPAIOx8IxaHz+fU26ER5vK4zl7Hq3SPUFX2TnJWH1HNYvBEdUtGCrBpGET2n1AIC5Cng8kvHCpNUyhy0hwDp8CmEMZ0h8AJACabxuB/6IaFT/8NKm9lrSorUNVq+5p8t1p11/FyZ1Zfff7/lqi6MHiiuiP3IvDvByGKrkEBoEgo4KMQUF/LwNVrfmhQun2L+4AXtkFx+D8Q2yZKgVMRFBhjmoRTmvuwkLkjAOrGmny3WnXW8WJPIFHNYvBEdcfVTFiLzVDCFgT5KIQUQDXANXlbdUNg4DLbv7sPgwKA2DYRUACH/vYhRrd/pN4NzZXHnsszdu0R23tVYl99mGnoadgTSFSzmDBOdYYlshtGe7+DIqGUepF8FI79JMJXA5jy5Enk3YdB8fhSKEbuwt39hyOmVQgDgVJq65p8dZG9J7Cs/0MVqNs1x4hqmkIIUZl1PusFo9EIjUYDg8GAwMDAmr4cqkDSH1kY8smv6Ixz+FydAB+F1Wk7k1841CoVYLgIBLcERuy01YEil7CukGewz7YDnPcEMqCl+qy6v7/Z80R1hj2/4xhaY0VRf4f9xcL2taK+lgFYi22z8AIaA2qHbCgqhz2X5/EuTdhDV4PYE0hUc5jzRLVW6R6Q0AZqAMAg7MU4n22ytkIA3iVyoJCnAxpGAI8tAXw1NXH5RDetumf1EZFzDJ6oVnJWWVnj543eisP4h+oTKefJCH/44xp8FELKgRIKLyiUXrbgKZB/nVPtVp2z+ojIOQZPVOvYcz1KJ+s1v3Yay9XvSYHTTNNw7MZdCEeOlAMlACga3QYMWAY0bsteJyIichtznqhWsVgFpm854bTW0BUEwQQVioQCo0yvYh3ioEcIjqE1njYnwKrwgsLHD3j6P0CzvzFwIiKiSmHPE9UqS/acRW5BkdN9OoTiIdMihCIXJ9Batu830RrJcZ/jzvZtgKBmt+JSiYiojmLPE9Ua5mIrVvzvz3Lb6BDqEDjZpfq2Y+BEREQ3jcET1QqJyTr0nLcb+SZLpc/BastERFQVOGxHHq+sBHFXcd01IiKqSux5Io9msQrM2ZZyU4ETwHXXiIio6jB4Io92IDVbVsupIqXDI1ZbJiKiqsZhO/Jo9iVXXPXivS3xYHstqy0TEVG1YfBEHs3dJO9//y8N3Vs0wuNduNAvERFVDw7bkUeyWAV+PnsF+879hQC1l1vHztmWAou1sllSRERE5WPPE3mcxGQdpm85UWYxzPIIADpDIQ6kZnO9LyIiqhYMnsijJCbrMGbtkZs+j7u5UkRERK7isB15DItVIOHrk1VyLhbEJCKi6sKeJ/IYB1KzoTeabuocLIhJRETVjT1P5DHcHWorXYCABTGJiOhWYPBEHsOdobYpsW2g1cjbsyAmERHdChy2I4/RI6oRtIHqCofuIjS+mPBgG0x4sA0OpGazICYREd1SDJ7IY3gpFUh47I4KZ9uVHJZjOQIiIrrVOGxHHqVvxwgsf64bgvx9HPYF+/tgOYfliIiohrkVPLVs2RIKhcLhMX78eGRnZ2PixIlo164d/Pz80Lx5c7zyyiswGAyyczg7fsOGDbI2P/zwA7p16wa1Wo3WrVtjzZo1Dtfy8ccfo2XLlvD19UV0dDQOHDgg219YWIjx48cjJCQEDRo0wKBBg5CRkeHO7VIN6dsxAodnPoR1o6Ix4YFWmPBAa6x7MRqHZj7EwImIiGqcW8N2Bw8ehMVikZ4nJyfjoYcewlNPPYXLly/j8uXL+Mc//oEOHTrg/PnzGDNmDC5fvozPP/9cdp7Vq1ejb9++0vOgoCDp36mpqejfvz/GjBmDdevWYffu3XjxxRcRERGBuLg4AMDGjRsxdepULF++HNHR0fjggw8QFxeHM2fOICwsDAAwZcoU7NixA5s3b4ZGo8GECRMwcOBA/Pzzz26/SXTreSkVuKdNKO5pE1rTl0JERCQnbsKkSZNEq1athNVqdbp/06ZNQqVSiaKiImkbALF169Yyz/nGG2+IO+64Q7btmWeeEXFxcdLzHj16iPHjx0vPLRaLiIyMFPPmzRNCCJGbmyt8fHzE5s2bpTanTp0SAERSUpLL92cwGAQAYTAYXD6GiIiIalZ1f39XOufJbDZj7dq1GDlyJBQK5zOcDAYDAgMD4e0t7+AaP348QkND0aNHD6xatQpC3FjENSkpCbGxsbL2cXFxSEpKkl738OHDsjZKpRKxsbFSm8OHD6OoqEjWpn379mjevLnUxhmTyQSj0Sh7EBEREZVU6dl2X375JXJzczFixAin+69cuYK5c+fipZdekm1/66238OCDD8Lf3x/fffcdxo0bh6tXr+KVV14BAOj1eoSHh8uOCQ8Ph9FoxLVr15CTkwOLxeK0zenTp6VzqFQq2XCgvY1ery/znubNm4c5c+a4cvtERERUT1U6eFq5ciX69euHyMhIh31GoxH9+/dHhw4dkJCQINsXHx8v/btr167Iz8/HokWLpOCpJs2YMQNTp06VnhuNRjRr1qwGr4iIiIg8TaWG7c6fP4/vv/8eL774osO+vLw89O3bFw0bNsTWrVvh4+M45byk6OhopKenw2SyFUbUarUOs+IyMjIQGBgIPz8/hIaGwsvLy2kbrVYrncNsNiM3N7fMNs6o1WoEBgbKHkREREQlVSp4Wr16NcLCwtC/f3/ZdqPRiD59+kClUuHrr7+Gr2/Fy20cPXoUwcHBUKvVAICYmBjs3r1b1mbXrl2IiYkBAKhUKnTv3l3Wxmq1Yvfu3VKb7t27w8fHR9bmzJkzuHDhgtSGiIiIqDLcHrazWq1YvXo1hg8fLksEtwdOBQUFWLt2rSzhunHjxvDy8sK2bduQkZGBnj17wtfXF7t27cK7776L1157TTrPmDFjsGTJErzxxhsYOXIk9uzZg02bNmHHjh1Sm6lTp2L48OG466670KNHD3zwwQfIz8/HCy+8AADQaDQYNWoUpk6dikaNGiEwMBATJ05ETEwMevbsWek3i4iIiMjtUgXffvutACDOnDkj2753714BwOkjNTVVCCHEN998I7p06SIaNGggAgICROfOncXy5cuFxWJxOFeXLl2ESqUSt912m1i9erXDdXz00UeiefPmQqVSiR49eohff/1Vtv/atWti3LhxIjg4WPj7+4snnnhC6HQ6t+6VpQqIiIhqn+r+/lYIUaJOAMkYjUZoNBqp5AIRERF5vur+/ubCwOWwx5Ws90RERFR72L+3q6t/iMFTOfLy8gCA5QqIiIhqoby8PGg0mio/L4ftymG1WnH58mU0bNiwzCrqlWWvIXXx4sU6PSRYH+6zPtwjUD/usz7cI1A/7pP3WHdU5j6FEMjLy0NkZCSUykovplIm9jyVQ6lUomnTptX6GvWlnlR9uM/6cI9A/bjP+nCPQP24T95j3eHufVZHj5Nd1YdjRERERHUYgyciIiIiNzB4qiFqtRqzZ8+WKqvXVfXhPuvDPQL14z7rwz0C9eM+eY91hyfeJxPGiYiIiNzAniciIiIiNzB4IiIiInIDgyciIiIiNzB4IiIiInIDg6cytGzZEgqFwuExfvx4AEBhYSHGjx+PkJAQNGjQAIMGDUJGRobsHBcuXED//v3h7++PsLAwvP766yguLpa1+eGHH9CtWzeo1Wq0bt0aa9ascbiWjz/+GC1btoSvry+io6Nx4MAB2X5XrqUy95mdnY2JEyeiXbt28PPzQ/PmzfHKK6/AYDDIzuHs+A0bNnjMfVb0Wd5///0O+8aMGSM7R23/LNPS0pzuUygU2Lx5s3QOT/8sLRYL4uPjERUVBT8/P7Rq1Qpz586VrV8lhMCsWbMQEREBPz8/xMbG4uzZs7LzZGdnY+jQoQgMDERQUBBGjRqFq1evytocP34c9957L3x9fdGsWTMsXLjQ4Xo2b96M9u3bw9fXF506dcLOnTtl+125FnfvsaioCNOmTUOnTp0QEBCAyMhIDBs2DJcvX5adx9n/D/Pnz/eIe3TlPgFgxIgRDvfQt29f2Xlq82cJOP+ZUygUWLRokdTG0z/LvLw8TJ48GS1atICfnx/uvvtuHDx40K3zevLn6JQgpzIzM4VOp5Meu3btEgDE3r17hRBCjBkzRjRr1kzs3r1bHDp0SPTs2VPcfffd0vHFxcWiY8eOIjY2Vvz2229i586dIjQ0VMyYMUNq8+effwp/f38xdepUkZKSIj766CPh5eUlEhMTpTYbNmwQKpVKrFq1Spw8eVKMHj1aBAUFiYyMDKlNRddS2fs8ceKEGDhwoPj666/FuXPnxO7du0WbNm3EoEGDZOcAIFavXi07z7Vr1zzmPiv6LP/v//5PjB49WtbGYDBIx9eFz7K4uFi2T6fTiTlz5ogGDRqIvLy8WvNZvvPOOyIkJERs375dpKamis2bN4sGDRqIDz/8UGozf/58odFoxJdffimOHTsmHnvsMREVFSW7j759+4rOnTuLX3/9Vfzvf/8TrVu3FkOGDJH2GwwGER4eLoYOHSqSk5PF+vXrhZ+fn/jXv/4ltfn555+Fl5eXWLhwoUhJSREzZ84UPj4+4sSJE25di7v3mJubK2JjY8XGjRvF6dOnRVJSkujRo4fo3r277DwtWrQQb731luyzvHr1qkfco6uf5fDhw0Xfvn1l95CdnS07T23+LIUQDj+Xq1atEgqFQvzxxx+15rN8+umnRYcOHcSPP/4ozp49K2bPni0CAwNFenq6y+f15M/RGQZPLpo0aZJo1aqVsFqtIjc3V/j4+IjNmzdL+0+dOiUAiKSkJCGEEDt37hRKpVLo9XqpzbJly0RgYKAwmUxCCCHeeOMNcccdd8he55lnnhFxcXHS8x49eojx48dLzy0Wi4iMjBTz5s0TQgiXrqWy9+nMpk2bhEqlEkVFRdI2AGLr1q1lntPT7rP0Pf7f//2fmDRpUpnt6+pn2aVLFzFy5EjZNk//LPv37+9wzQMHDhRDhw4VQghhtVqFVqsVixYtkvbn5uYKtVot1q9fL4QQIiUlRQAQBw8elNp88803QqFQiEuXLgkhhFi6dKkIDg6WPl8hhJg2bZpo166d9Pzpp58W/fv3l11LdHS0ePnll12+lsrcozMHDhwQAMT58+elbS1atBDvv/9+mcfU5D26ep/Dhw8Xjz/+eJnnqIuf5eOPPy4efPBB2TZP/iwLCgqEl5eX2L59u2x7t27dxJtvvlknfiad4bCdC8xmM9auXYuRI0dCoVDg8OHDKCoqQmxsrNSmffv2aN68OZKSkgAASUlJ6NSpE8LDw6U2cXFxMBqNOHnypNSm5DnsbeznMJvNOHz4sKyNUqlEbGys1MaVa6nsfTpjMBgQGBgIb2/5sojjx49HaGgoevTogVWrVsm6pT3pPsu6x3Xr1iE0NBQdO3bEjBkzUFBQILv+uvZZHj58GEePHsWoUaMc9nnyZ3n33Xdj9+7d+P333wEAx44dw759+9CvXz8AQGpqKvR6vezcGo0G0dHRsp/NoKAg3HXXXVKb2NhYKJVK7N+/X2pz3333QaVSye7zzJkzyMnJcem9cOVaKnOPzhgMBigUCgQFBcm2z58/HyEhIejatSsWLVokG2quyXt05z5/+OEHhIWFoV27dhg7diyysrJk91CXPsuMjAzs2LHD6c+lp36WxcXFsFgs8PX1lW338/PDvn376sTPpDNcGNgFX375JXJzczFixAgAgF6vh0qlcvhFFR4eDr1eL7Up+WVr32/fV14bo9GIa9euIScnBxaLxWmb06dPu3wtlb3P0q5cuYK5c+fipZdekm1/66238OCDD8Lf3x/fffcdxo0bh6tXr+KVV17xuPt0do/PPvssWrRogcjISBw/fhzTpk3DmTNnsGXLlnKv377P0+6xrPssaeXKlbj99ttx9913y7Z7+mc5ffp0GI1GtG/fHl5eXrBYLHjnnXcwdOhQ6dz2c5V1br1ej7CwMNl+b29vNGrUSNYmKirK4Rz2fcHBwWW+FyXPUdG1VOYeSyssLMS0adMwZMgQ2aKpr7zyCrp164ZGjRrhl19+wYwZM6DT6fDee+/V+D26ep99+/bFwIEDERUVhT/++AN///vf0a9fPyQlJcHLy6vOfZaffvopGjZsiIEDB8q2e/Jn2bBhQ8TExGDu3Lm4/fbbER4ejvXr1yMpKQmtW7euEz+TzjB4csHKlSvRr18/REZG1vSlVKvy7tNoNKJ///7o0KEDEhISZPvi4+Olf3ft2hX5+flYtGiR9IXrSZzdY8lgsFOnToiIiEDv3r3xxx9/oFWrVjVxmTetvM/y2rVr+Oyzz2Sfm52nf5abNm3CunXr8Nlnn+GOO+7A0aNHMXnyZERGRmL48OE1fXlVwp17LCoqwtNPPw0hBJYtWybbN3XqVOnfd955J1QqFV5++WXMmzfPI5a5cOU+Bw8eLLXv1KkT7rzzTrRq1Qo//PADevfuXVOX7jJ3/39dtWoVhg4d6tCL4+mf5X//+1+MHDkSTZo0gZeXF7p164YhQ4bg8OHDNX1p1YbDdhU4f/48vv/+e7z44ovSNq1WC7PZjNzcXFnbjIwMaLVaqU3p2UP25xW1CQwMhJ+fH0JDQ+Hl5eW0TclzVHQtlb1Pu7y8PPTt2xcNGzbE1q1b4ePjU+65oqOjkZ6eDpPJ5FH3Wd49lr5+ADh37ly512/f50n36Mp9fv755ygoKMCwYcMqPJenfZavv/46pk+fjsGDB6NTp054/vnnMWXKFMybN086t/1c5b1+ZmambH9xcTGys7Or5Oe35P6KrqUy92hnD5zOnz+PXbt2yXqdnImOjkZxcTHS0tJq/B7duc+SbrvtNoSGhsp+NuvCZwkA//vf/3DmzJkKfz8BnvdZtmrVCj/++COuXr2Kixcv4sCBAygqKsJtt91WJ34mnWHwVIHVq1cjLCwM/fv3l7Z1794dPj4+2L17t7TtzJkzuHDhAmJiYgAAMTExOHHihOx/CPsvuA4dOkhtSp7D3sZ+DpVKhe7du8vaWK1W7N69W2rjyrVU9j4BW49Tnz59oFKp8PXXXzv8ReTM0aNHERwcLP1F5Cn3WdY9Ort+AIiIiJCuvy58lnYrV67EY489hsaNG1d4Lk/7LAsKCqBUyn9teXl5wWq1AgCioqKg1Wpl5zYajdi/f7/sZzM3N1f2V/GePXtgtVqlwDkmJgY//fQTioqKZPfZrl07BAcHu/ReuHItlblH4EbgdPbsWXz//fcICQkp83x2R48ehVKplIZHavIeXb3P0tLT05GVlSX72aztn6XdypUr0b17d3Tu3LnM89l52mdpFxAQgIiICOTk5ODbb7/F448/Xid+Jp1yK728nrFYLKJ58+Zi2rRpDvvGjBkjmjdvLvbs2SMOHTokYmJiRExMjLTfPr29T58+4ujRoyIxMVE0btzY6fT2119/XZw6dUp8/PHHTqd9q9VqsWbNGpGSkiJeeuklERQUJJv5VdG1VPY+DQaDiI6OFp06dRLnzp2TTZMtLi4WQgjx9ddfi08++UScOHFCnD17VixdulT4+/uLWbNmedR9lnWP586dE2+99ZY4dOiQSE1NFV999ZW47bbbxH333Se1qQufpd3Zs2eFQqEQ33zzjcO+2vBZDh8+XDRp0kSa+r1lyxYRGhoq3njjDanN/PnzRVBQkPjqq6/E8ePHxeOPP+50WnTXrl3F/v37xb59+0SbNm1k06Jzc3NFeHi4eP7550VycrLYsGGD8Pf3d5gW7e3tLf7xj3+IU6dOidmzZzudFl3Rtbh7j2azWTz22GOiadOm4ujRo7KfS/tMpF9++UW8//774ujRo+KPP/4Qa9euFY0bNxbDhg3ziHt05T7z8vLEa6+9JpKSkkRqaqr4/vvvRbdu3USbNm1EYWFhnfgs7QwGg/D39xfLli1zOEdt+CwTExPFN998I/7880/x3Xffic6dO4vo6GhhNptdPq8nf47OMHgqx7fffisAiDNnzjjsu3btmhg3bpwIDg4W/v7+4oknnhA6nU7WJi0tTfTr10/4+fmJ0NBQ8eqrr8qm+AshxN69e0WXLl2ESqUSt912m1i9erXDa3300UeiefPmQqVSiR49eohff/3V7WupzH3u3btXAHD6SE1NFULYppN26dJFNGjQQAQEBIjOnTuL5cuXC4vF4lH3WdY9XrhwQdx3332iUaNGQq1Wi9atW4vXX39dVudJiNr/WdrNmDFDNGvWzOHzEaJ2fJZGo1FMmjRJNG/eXPj6+orbbrtNvPnmm7Lpy1arVcTHx4vw8HChVqtF7969Hd6PrKwsMWTIENGgQQMRGBgoXnjhBVm9KyGEOHbsmOjVq5dQq9WiSZMmYv78+Q7Xs2nTJtG2bVuhUqnEHXfcIXbs2CHb78q1uHuPqampZf5c2muXHT58WERHRwuNRiN8fX3F7bffLt59911Z0FGT9+jKfRYUFIg+ffqIxo0bCx8fH9GiRQsxevRoWRAuRO3+LO3+9a9/CT8/P5Gbm+twjtrwWW7cuFHcdtttQqVSCa1WK8aPHy+7l9r+M+mMQogS85CJiIiIqFzMeSIiIiJyA4MnIiIiIjcweCIiIiJyA4MnIiIiIjcweCIiIiJyA4MnIiIiIjcweCIiIiJyA4MnIiIiIjcweCIiIiJyA4MnIiIiIjcweCIiIiJyA4MnIiIiIjf8PymCenbvku4xAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "reclassifying HD 1\n",
      "reclassifying HD 3108\n",
      "reclassifying HD 8772\n",
      "There are 167 HDs still with both discontinuity problems\n",
      "Additionally, 1046 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%200 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for i, t in enumerate(sPgenerator):    \n",
    "    if i%500 == 0:\n",
    "        print(\"reclassifying HD\",t)\n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4037f8cb-661b-4032-bfb9-e147975e3bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\n",
      "working on enclave-generating HD 0\n",
      "working on enclave-generating HD 831\n",
      "working on enclave-generating HD 2369\n",
      "working on enclave-generating HD 3816\n",
      "working on enclave-generating HD 4981\n",
      "working on enclave-generating HD 8578\n",
      "working on enclave-generating HD 9356\n",
      "working on enclave-generating HD 13097\n",
      "working on enclave-generating HD 2579\n",
      "working on enclave-generating HD 9062\n",
      "Out of 4776 HDpolys that generated enclaves, 0 had contiguous adjrs, while 4629 neighbored a state boundary while 147 had only internal enclaves\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required 0\n"
     ]
    }
   ],
   "source": [
    "print(\"1/13/24 - classify further: ID those with adjoiners intersecting the map boundary vs internal islands\")\n",
    "internals, edgers, nOK = list(), list(), 0\n",
    "for i,t in enumerate(eLgenerator):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on enclave-generating HD\",t)\n",
    "    twoNbrs = get2nbrs(HDunitList[t],unitNbrs)\n",
    "    isContig, adjSublist = isContiguous(twoNbrs,unitNbrs)\n",
    "    if isContig:\n",
    "        nOK +=1\n",
    "    else:\n",
    "        if len (set(getAdjoiners(HDunitList[t],unitNbrs)).intersection(set(borderUnits)))  > 0:\n",
    "            edgers.append(t)\n",
    "        else:\n",
    "            internals.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDpolys that generated enclaves,\",nOK,\"had contiguous adjrs, while\",len(edgers),\n",
    "      \"neighbored a state boundary while\",len(internals),\"had only internal enclaves\")\n",
    " \n",
    "plotEnclaves = input(\"enter 1 to print the total enclave pops for all enclavy HDs.  This takes a few min; not required\")\n",
    "if str(plotEnclaves) == str(1):\n",
    "    for i,t in enumerate(internals): \n",
    "        if i%500 == 0:\n",
    "            print(\"computing full enclave lists for HD\",t)\n",
    "        fullEnclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        tEpop = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in fullEnclaveLists ] ) \n",
    "        plt.scatter(HDvPop[t],tEpop)\n",
    "    plt.plot([aDP,aDP],[0,5000],ls=\"--\")\n",
    "    print(\"Here are the total enclave pops for enclaving HDs not touching the state boundary vs. the original HD pop\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "05f929ce-4f94-47a6-baf4-60c5a89e2ae5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 816392 district pop vs 777517 target\n",
      "working on enclave-y HD 555 . We have evaluated 0 of 4776 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 9499 . We have evaluated 100 of 4776 enclavy HDs.Time is now 16\n",
      "working on enclave-y HD 77 . We have evaluated 200 of 4776 enclavy HDs.Time is now 44\n",
      "working on enclave-y HD 232 . We have evaluated 300 of 4776 enclavy HDs.Time is now 67\n",
      "working on enclave-y HD 403 . We have evaluated 400 of 4776 enclavy HDs.Time is now 80\n",
      "working on enclave-y HD 575 . We have evaluated 500 of 4776 enclavy HDs.Time is now 94\n",
      "working on enclave-y HD 749 . We have evaluated 600 of 4776 enclavy HDs.Time is now 109\n",
      "working on enclave-y HD 906 . We have evaluated 700 of 4776 enclavy HDs.Time is now 130\n",
      "working on enclave-y HD 1083 . We have evaluated 800 of 4776 enclavy HDs.Time is now 156\n",
      "working on enclave-y HD 1218 . We have evaluated 900 of 4776 enclavy HDs.Time is now 179\n",
      "working on enclave-y HD 1617 . We have evaluated 1000 of 4776 enclavy HDs.Time is now 205\n",
      "working on enclave-y HD 2223 . We have evaluated 1100 of 4776 enclavy HDs.Time is now 212\n",
      "working on enclave-y HD 2477 . We have evaluated 1200 of 4776 enclavy HDs.Time is now 232\n",
      "working on enclave-y HD 2765 . We have evaluated 1300 of 4776 enclavy HDs.Time is now 253\n",
      "working on enclave-y HD 3081 . We have evaluated 1400 of 4776 enclavy HDs.Time is now 279\n",
      "working on enclave-y HD 3273 . We have evaluated 1500 of 4776 enclavy HDs.Time is now 313\n",
      "working on enclave-y HD 3722 . We have evaluated 1600 of 4776 enclavy HDs.Time is now 336\n",
      "working on enclave-y HD 3999 . We have evaluated 1700 of 4776 enclavy HDs.Time is now 358\n",
      "working on enclave-y HD 4525 . We have evaluated 1800 of 4776 enclavy HDs.Time is now 383\n",
      "working on enclave-y HD 4674 . We have evaluated 1900 of 4776 enclavy HDs.Time is now 405\n",
      "working on enclave-y HD 4832 . We have evaluated 2000 of 4776 enclavy HDs.Time is now 420\n",
      "working on enclave-y HD 4982 . We have evaluated 2100 of 4776 enclavy HDs.Time is now 436\n",
      "working on enclave-y HD 5141 . We have evaluated 2200 of 4776 enclavy HDs.Time is now 458\n",
      "working on enclave-y HD 5280 . We have evaluated 2300 of 4776 enclavy HDs.Time is now 497\n",
      "working on enclave-y HD 5448 . We have evaluated 2400 of 4776 enclavy HDs.Time is now 519\n",
      "working on enclave-y HD 5587 . We have evaluated 2500 of 4776 enclavy HDs.Time is now 540\n",
      "working on enclave-y HD 8619 . We have evaluated 2600 of 4776 enclavy HDs.Time is now 556\n",
      "working on enclave-y HD 8856 . We have evaluated 2700 of 4776 enclavy HDs.Time is now 570\n",
      "working on enclave-y HD 9019 . We have evaluated 2800 of 4776 enclavy HDs.Time is now 587\n",
      "working on enclave-y HD 9155 . We have evaluated 2900 of 4776 enclavy HDs.Time is now 611\n",
      "working on enclave-y HD 9292 . We have evaluated 3000 of 4776 enclavy HDs.Time is now 634\n",
      "working on enclave-y HD 9419 . We have evaluated 3100 of 4776 enclavy HDs.Time is now 665\n",
      "working on enclave-y HD 9566 . We have evaluated 3200 of 4776 enclavy HDs.Time is now 694\n",
      "working on enclave-y HD 9770 . We have evaluated 3300 of 4776 enclavy HDs.Time is now 703\n",
      "working on enclave-y HD 9936 . We have evaluated 3400 of 4776 enclavy HDs.Time is now 716\n",
      "working on enclave-y HD 10090 . We have evaluated 3500 of 4776 enclavy HDs.Time is now 749\n",
      "working on enclave-y HD 13888 . We have evaluated 3600 of 4776 enclavy HDs.Time is now 765\n",
      "working on enclave-y HD 14074 . We have evaluated 3700 of 4776 enclavy HDs.Time is now 775\n",
      "working on enclave-y HD 181 . We have evaluated 3800 of 4776 enclavy HDs.Time is now 792\n",
      "working on enclave-y HD 782 . We have evaluated 3900 of 4776 enclavy HDs.Time is now 808\n",
      "working on enclave-y HD 1840 . We have evaluated 4000 of 4776 enclavy HDs.Time is now 830\n",
      "working on enclave-y HD 2959 . We have evaluated 4100 of 4776 enclavy HDs.Time is now 859\n",
      "working on enclave-y HD 3824 . We have evaluated 4200 of 4776 enclavy HDs.Time is now 883\n",
      "working on enclave-y HD 4798 . We have evaluated 4300 of 4776 enclavy HDs.Time is now 905\n",
      "working on enclave-y HD 5430 . We have evaluated 4400 of 4776 enclavy HDs.Time is now 927\n",
      "working on enclave-y HD 9058 . We have evaluated 4500 of 4776 enclavy HDs.Time is now 947\n",
      "working on enclave-y HD 9549 . We have evaluated 4600 of 4776 enclavy HDs.Time is now 975\n",
      "working on enclave-y HD 13075 . We have evaluated 4700 of 4776 enclavy HDs.Time is now 995\n",
      "Out of 4776 HDs with enclaves 4776 had enclaves.\n",
      "Of these, 4479 wouldn't be over 816392 if all enclaves filled, while 297 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8987ad1f-d372-415d-a2ec-8fc6736c5102",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "147 4629 1184 1006\n",
      "297 297 4479\n"
     ]
    }
   ],
   "source": [
    "print(len(internals),len(edgers),len(doubleTroubleList),len(set(edgers).intersection(set(doubleTroubleList))))\n",
    "print(len(set(edgers).difference(set(canFill))) ,len(cantFill) , len(canFill))\n",
    "#610 3379 2187 1954\n",
    "#80 80 3909  for original MN option3 (assigned CCBs by captured area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8fa887be-92e6-422a-8e80-e00fd3265aab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00734 0.15062\n",
      "CCB cluster 0 = unit 5839 with pop 127657.0 now has use of 1.0012681394747804\n",
      "CCB cluster 1 = unit 5840 with pop 164779.0 now has use of 1.0003704099074286\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e9d50ef8-39b2-4e22-a11e-cabeda78721e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b759b3c7-b8a3-4d2c-a8fe-50fa93b3e940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "551454fb-d57c-466f-8b31-39ccb2a728de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 297 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "for t in cantFill:\n",
    "    plotPoly(hdCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5269a224-4a97-417c-99af-6a98df0087ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 801\n",
      "working on avg unit-to-HDcp dist for HD 2098\n",
      "working on avg unit-to-HDcp dist for HD 3363\n",
      "working on avg unit-to-HDcp dist for HD 4848\n",
      "working on avg unit-to-HDcp dist for HD 7345\n",
      "working on avg unit-to-HDcp dist for HD 9331\n",
      "working on avg unit-to-HDcp dist for HD 13048\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([5839, 5840] )  #lock in the clusters  #update this for each state - see CCB report 3 blocks up\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6e2e44bd-a084-42f6-b533-5dc6d21912a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this code will solve large enclaves so HD and complement are contiguous for 297 HDs\n",
      "working on HD 0 cantFill 0 of 297 .Time is now 0\n",
      "working on HD 33 cantFill 20 of 297 .Time is now 4\n",
      "working on HD 789 cantFill 40 of 297 .Time is now 9\n",
      "working on HD 1410 cantFill 60 of 297 .Time is now 23\n",
      "working on HD 2218 cantFill 80 of 297 .Time is now 28\n",
      "working on HD 3087 cantFill 100 of 297 .Time is now 38\n",
      "working on HD 3115 cantFill 120 of 297 .Time is now 52\n",
      "working on HD 4907 cantFill 140 of 297 .Time is now 57\n",
      "working on HD 5246 cantFill 160 of 297 .Time is now 67\n",
      "working on HD 5273 cantFill 180 of 297 .Time is now 76\n",
      "working on HD 9234 cantFill 200 of 297 .Time is now 80\n",
      "working on HD 9439 cantFill 220 of 297 .Time is now 83\n",
      "working on HD 13094 cantFill 240 of 297 .Time is now 87\n",
      "working on HD 2950 cantFill 260 of 297 .Time is now 93\n",
      "working on HD 9436 cantFill 280 of 297 .Time is now 99\n",
      "after the MustFree code run, we have the following for cluster usage\n",
      "current avg use and its sd are 1.00563 0.14822\n",
      "CCB cluster 0 = unit 5839 with pop 127657.0 now has use of 1.0012681394747804\n",
      "CCB cluster 1 = unit 5840 with pop 164779.0 now has use of 1.0003704099074286\n"
     ]
    },
    {
     "data": {
      "image/png": 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jWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABgvYaYXAADX4nT/aEzmXZRilSPNFpO5AUQPwQLAaItSrLIlWlTb1hOT+W2JFh3aUEq0AIYjWAAYzZFm06ENpRr0TUR97tP9o6pt69Ggb4JgAQxHsAAwniPNRlAA8xwX3QIAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA400rWJqbm5WTk6Pk5GQ5nU4dO3bsiuP37NmjO++8U8nJybrnnnt04MCBS8a89957+tKXviS73a6UlBQVFRXpzJkz01keAACYYyIOlra2NtXV1amxsVHd3d3Kz89XWVmZ+vv7w44/evSo1qxZoyeeeEInTpxQeXm5ysvLdfLkyeCY//7v/9aKFSt055136vDhw/rpT3+qLVu2KDk5efpHBgAA5oy4QCAQiGQHp9OpoqIi7dy5U5Lk9/uVnZ2tdevWqb6+/pLxFRUV8vl82rdvX3Db/fffr4KCArW0tEiSHnvsMSUmJuq73/3utA5iZGREdrtdw8PDSk1NndYcuDYne4e1asfb2rduhfIc9pleDnBd+P8zMLMief2O6AzLxMSEurq65HK5Pp0gPl4ul0udnZ1h9+ns7AwZL0llZWXB8X6/X/v379dnP/tZlZWVKSMjQ06nU3v37r3sOsbHxzUyMhLyAAAAc1dEwTIwMKCpqSllZmaGbM/MzJTX6w27j9frveL4/v5+jY6O6vnnn9fDDz+sH/3oR/ryl7+sP/zDP9SRI0fCzul2u2W324OP7OzsSA4DAADMMjP+LiG/3y9J+oM/+AM988wzKigoUH19vVatWhX8ldFvamho0PDwcPBx9uzZG7lkAABwg0X0WULp6emyWCzq6+sL2d7X16esrKyw+2RlZV1xfHp6uhISEnT33XeHjLnrrrv09ttvh50zKSlJSUlJkSwdAADMYhGdYbFarSosLFRHR0dwm9/vV0dHh0pKSsLuU1JSEjJekg4ePBgcb7VaVVRUpFOnToWM+cUvfqFbb701kuUBAIA5KuJPa66rq1NVVZWWLVum4uJieTwe+Xw+VVdXS5IqKyvlcDjkdrslSevXr1dpaamampq0cuVK7d69W8ePH9euXbuCc27cuFEVFRV64IEH9NBDD6m9vV3//u//rsOHD0fnKAEAwKwWcbBUVFTo3Llz2rp1q7xerwoKCtTe3h68sPbMmTOKj//0xM3y5cvV2tqqzZs3a9OmTcrNzdXevXuVl5cXHPPlL39ZLS0tcrvd+sY3vqE77rhD//qv/6oVK1ZE4RABAMBsF/F9WEzEfVgu1Ts0pkHfRNTnPd0/qtq2Hu5bgTmB+7AAMyuS1++Iz7DAfL1DY3I1HdHY5FRM5rclWrQoxRqTuQEACIdgmYMGfRMam5ySp6JASzMWRH3+RSlWOdJsUZ8XAIDLIVjmsKUZCzjNDQCYE2b8xnEAAABXQ7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjMedbgHMe6f7R2MyLx9jAUQPwQJg3lqUYpUt0aLatp6YzG9LtOjQhlKiBYgCggXAvOVIs+nQhlIN+iaiPvfp/lHVtvVo0DdBsABRQLAAmNccaTaCApgFuOgWAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGC8aQVLc3OzcnJylJycLKfTqWPHjl1x/J49e3TnnXcqOTlZ99xzjw4cOHDZsWvXrlVcXJw8Hs90lgYAAOagiIOlra1NdXV1amxsVHd3t/Lz81VWVqb+/v6w448ePao1a9boiSee0IkTJ1ReXq7y8nKdPHnykrGvvfaa3nnnHS1ZsiTyIwEAAHNWxMGyfft2PfXUU6qurtbdd9+tlpYWfeYzn9HLL78cdvyLL76ohx9+WBs3btRdd92l5557Tvfdd5927twZMq63t1fr1q3Tv/zLvygxMXF6RwMAAOakiIJlYmJCXV1dcrlcn04QHy+Xy6XOzs6w+3R2doaMl6SysrKQ8X6/X48//rg2btyoz33uc1ddx/j4uEZGRkIeAABg7oooWAYGBjQ1NaXMzMyQ7ZmZmfJ6vWH38Xq9Vx3/wgsvKCEhQd/4xjeuaR1ut1t2uz34yM7OjuQwAADALDPj7xLq6urSiy++qFdffVVxcXHXtE9DQ4OGh4eDj7Nnz8Z4lQAAYCZFFCzp6emyWCzq6+sL2d7X16esrKyw+2RlZV1x/I9//GP19/frlltuUUJCghISEvTRRx9pw4YNysnJCTtnUlKSUlNTQx4AAGDuiihYrFarCgsL1dHREdzm9/vV0dGhkpKSsPuUlJSEjJekgwcPBsc//vjj+ulPf6qenp7gY8mSJdq4caN++MMfRno8AABgDkqIdIe6ujpVVVVp2bJlKi4ulsfjkc/nU3V1tSSpsrJSDodDbrdbkrR+/XqVlpaqqalJK1eu1O7du3X8+HHt2rVLkrR48WItXrw45GckJiYqKytLd9xxx/UeHwAAmAMiDpaKigqdO3dOW7duldfrVUFBgdrb24MX1p45c0bx8Z+euFm+fLlaW1u1efNmbdq0Sbm5udq7d6/y8vKidxQAAGBOizhYJKmmpkY1NTVhv3f48OFLtq1evVqrV6++5vk//PDD6SwLAADMUTP+LiEAAICrIVgAAIDxCBYAAGC8aV3DAgCYOb1DYxr0TcRk7kUpVjnSbDGZG7geBAsAzCK9Q2NyNR3R2ORUTOa3JVp0aEMp0QLjECwAMIsM+iY0NjklT0WBlmYsiOrcp/tHVdvWo0HfBMEC4xAsABBDp/tHYzLf0owFynPYozo3YDKCBQBiYFGKVbZEi2rbeqI+ty3RokUp1qjPC5iMYAGAGHCk2XRoQ2lMLo7lwljMRwQLAMSII81GWABRwn1YAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPESZnoB813v0JgGfRNRnfN0/2hU5wMwv8Ti75BFKVY50mxRnxfzx7SCpbm5Wd/+9rfl9XqVn5+vHTt2qLi4+LLj9+zZoy1btujDDz9Ubm6uXnjhBT3yyCOSpMnJSW3evFkHDhzQ+++/L7vdLpfLpeeff15LliyZ3lHNEr1DY3I1HdHY5FTU57YlWrQoxRr1eQHMXYtSrLIlWlTb1hP1uW2JFh3aUEq0YNoiDpa2tjbV1dWppaVFTqdTHo9HZWVlOnXqlDIyMi4Zf/ToUa1Zs0Zut1urVq1Sa2urysvL1d3drby8PF24cEHd3d3asmWL8vPzNTg4qPXr1+tLX/qSjh8/HpWDNNWgb0Jjk1PyVBRoacaCqM7Nf80AiJQjzaZDG0pjcta3tq1Hg74J/l7CtMUFAoFAJDs4nU4VFRVp586dkiS/36/s7GytW7dO9fX1l4yvqKiQz+fTvn37gtvuv/9+FRQUqKWlJezP+M///E8VFxfro48+0i233HLVNY2MjMhut2t4eFipqamRHM6MOtk7rFU73ta+dSuU57DP9HIAICb4uw6XE8nrd0QX3U5MTKirq0sul+vTCeLj5XK51NnZGXafzs7OkPGSVFZWdtnxkjQ8PKy4uDilpaWF/f74+LhGRkZCHgAAYO6KKFgGBgY0NTWlzMzMkO2ZmZnyer1h9/F6vRGN//Wvf61vfetbWrNmzWVry+12y263Bx/Z2dmRHAYAAJhljHpb8+TkpL761a8qEAjopZdeuuy4hoYGDQ8PBx9nz569gasEAAA3WkQX3aanp8tisaivry9ke19fn7KyssLuk5WVdU3jL8bKRx99pDfeeOOKv8tKSkpSUlJSJEsHAACzWERnWKxWqwoLC9XR0RHc5vf71dHRoZKSkrD7lJSUhIyXpIMHD4aMvxgrv/zlL3Xo0CEtXrw4kmUBAIA5LuK3NdfV1amqqkrLli1TcXGxPB6PfD6fqqurJUmVlZVyOBxyu92SpPXr16u0tFRNTU1auXKldu/erePHj2vXrl2SPomVP/qjP1J3d7f27dunqamp4PUtN910k6xW7iUCAMB8F3GwVFRU6Ny5c9q6dau8Xq8KCgrU3t4evLD2zJkzio//9MTN8uXL1draqs2bN2vTpk3Kzc3V3r17lZeXJ0nq7e3V66+/LkkqKCgI+VlvvvmmHnzwwWkeGgAAmCumdafbmpoa1dTUhP3e4cOHL9m2evVqrV69Ouz4nJwcRXgrGAAAMM8Y9S4hAACAcAgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxpvWhx8CABCp0/2jMZl3UYpVjjRbTOaGOQgWAEBMLUqxypZoUW1bT0zmtyVadGhDKdEyxxEsAICYcqTZdGhDqQZ9E1Gf+3T/qGrbejTomyBY5jiCBQAQc440G0GB68JFtwAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMF7CTC8AAIDrdbp/NOpzLkqxypFmi/q8mB6C5Rr0Do1p0DcR9Xlj8QcMAOaTRSlW2RItqm3rifrctkSLDm0oJVoMQbBcRe/QmFxNRzQ2ORWT+W2JFi1KscZkbgCY6xxpNh3aUBr1/6g83T+q2rYeDfomCBZDECxXMeib0NjklDwVBVqasSDq83PKEQCujyPNxt+j8wDBco2WZixQnsM+08sAAGBe4l1CAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIzHfVgAALiMWH2ECjcNjRzBAgDAb4jlZxRJn3wsS8vjhVoc5Y9mmcshNK1gaW5u1re//W15vV7l5+drx44dKi4uvuz4PXv2aMuWLfrwww+Vm5urF154QY888kjw+4FAQI2NjfqHf/gHDQ0N6fOf/7xeeukl5ebmTmd5AABcl1h9RpEk/Y9vQmu/26Wql49Ffe5YhZA08zEUcbC0tbWprq5OLS0tcjqd8ng8Kisr06lTp5SRkXHJ+KNHj2rNmjVyu91atWqVWltbVV5eru7ubuXl5UmS/vZv/1Z/93d/p3/6p3/Sbbfdpi1btqisrEzvvvuukpOTr/8oAQCIUCw/oygWMRTLEJJm/tOr4wKBQCCSHZxOp4qKirRz505Jkt/vV3Z2ttatW6f6+vpLxldUVMjn82nfvn3Bbffff78KCgrU0tKiQCCgJUuWaMOGDfrzP/9zSdLw8LAyMzP16quv6rHHHrvqmkZGRmS32zU8PKzU1NRIDueqTvYOa9WOt7Vv3Qo+SwgAYLTeobGYnBW6+OnV0X4tjOT1O6IzLBMTE+rq6lJDQ0NwW3x8vFwulzo7O8Pu09nZqbq6upBtZWVl2rt3ryTpgw8+kNfrlcvlCn7fbrfL6XSqs7MzbLCMj49rfHw8+PXw8LCkTw482kbPj8g/fkGj50c0MhIX9fkBAIiWhfHSwoXRf60aPe+PyWvhxdftazl3ElGwDAwMaGpqSpmZmSHbMzMz9fOf/zzsPl6vN+x4r9cb/P7FbZcb85vcbreeffbZS7ZnZ2df24FMQ4knZlMDADArxOq18Pz587Lbr3zmZla+S6ihoSHkrI3f79evfvUrLV68WHFxnAW5HiMjI8rOztbZs2ej/us1RA/Pk/l4jmYHnqeZFQgEdP78eS1ZsuSqYyMKlvT0dFksFvX19YVs7+vrU1ZWVth9srKyrjj+4v/29fXpt3/7t0PGFBQUhJ0zKSlJSUlJIdvS0tIiORRcRWpqKn94ZwGeJ/PxHM0OPE8z52pnVi6K6E63VqtVhYWF6ujoCG7z+/3q6OhQSUlJ2H1KSkpCxkvSwYMHg+Nvu+02ZWVlhYwZGRnRT37yk8vOCQAA5peIfyVUV1enqqoqLVu2TMXFxfJ4PPL5fKqurpYkVVZWyuFwyO12S5LWr1+v0tJSNTU1aeXKldq9e7eOHz+uXbt2SZLi4uJUW1urv/qrv1Jubm7wbc1LlixReXl59I4UAADMWhEHS0VFhc6dO6etW7fK6/WqoKBA7e3twYtmz5w5o/j4T0/cLF++XK2trdq8ebM2bdqk3Nxc7d27N3gPFkn65je/KZ/Pp69//esaGhrSihUr1N7ezj1YZkBSUpIaGxsv+ZUbzMLzZD6eo9mB52n2iPg+LAAAADcan9YMAACMR7AAAADjESwAAMB4BAsAADAewTIPNTc3KycnR8nJyXI6nTp27Mqf7OnxeHTHHXfIZrMpOztbzzzzjH7961/foNXOP2+99ZYeffRRLVmyRHFxccHP3bqSw4cP67777lNSUpKWLl2qV199NebrnO8ifZ5+8IMf6Atf+IJ+67d+S6mpqSopKdEPf/jDG7PYeWo6f5Yu+o//+A8lJCRc9gamuPEIlnmmra1NdXV1amxsVHd3t/Lz81VWVqb+/v6w41tbW1VfX6/Gxka99957+sd//Ee1tbVp06ZNN3jl84fP51N+fr6am5uvafwHH3yglStX6qGHHlJPT49qa2v15JNP8mIYY5E+T2+99Za+8IUv6MCBA+rq6tJDDz2kRx99VCdOnIjxSuevSJ+ji4aGhlRZWanf//3fj9HKMB28rXmecTqdKioq0s6dOyV9cqfi7OxsrVu3TvX19ZeMr6mp0XvvvRdyJ+INGzboJz/5id5+++0btu75Ki4uTq+99toVb6L4rW99S/v379fJkyeD2x577DENDQ2pvb39BqwS1/I8hfO5z31OFRUV2rp1a2wWhqBInqPHHntMubm5slgs2rt3r3p6emK+PlwdZ1jmkYmJCXV1dcnlcgW3xcfHy+VyqbOzM+w+y5cvV1dXV/DXRu+//74OHDigRx555IasGVfX2dkZ8pxKUllZ2WWfU5jB7/fr/Pnzuummm2Z6Kfg/XnnlFb3//vtqbGyc6aXgN8zKT2vG9AwMDGhqaip4V+KLMjMz9fOf/zzsPl/72tc0MDCgFStWKBAI6H//93+1du1afiVkEK/XG/Y5HRkZ0djYmGw22wytDFeybds2jY6O6qtf/epMLwX/3y9/+UvV19frxz/+sRISeHk0DWdYcEWHDx/W3/zN3+jv//7v1d3drR/84Afav3+/nnvuuZleGjBrtba26tlnn9X3v/99ZWRkzPRyIGlqakpf+9rX9Oyzz+qzn/3sTC8HYZCQ80h6erosFov6+vpCtvf19SkrKyvsPlu2bNHjjz+uJ598UpJ0zz33BD/36S/+4i9CPjcKMyMrKyvsc5qamsrZFQPt3r1bTz75pPbs2XPJr/Iwc86fP6/jx4/rxIkTqqmpkfTJr+0CgYASEhL0ox/9SL/3e783w6uc33i1mUesVqsKCwtDLqD1+/3q6OhQSUlJ2H0uXLhwSZRYLBZJEtdrm6GkpCTkOZWkgwcPXvY5xcz53ve+p+rqan3ve9/TypUrZ3o5+D9SU1P1X//1X+rp6Qk+1q5dqzvuuEM9PT1yOp0zvcR5jzMs80xdXZ2qqqq0bNkyFRcXy+PxyOfzqbq6WpJUWVkph8Mht9stSXr00Ue1fft23XvvvXI6nTp9+rS2bNmiRx99NBguiK7R0VGdPn06+PUHH3ygnp4e3XTTTbrlllvU0NCg3t5efec735EkrV27Vjt37tQ3v/lN/emf/qneeOMNff/739f+/ftn6hDmhUifp9bWVlVVVenFF1+U0+mU1+uVJNlsNtnt9hk5hrkukucoPj5eeXl5IftnZGQoOTn5ku2YIQHMOzt27AjccsstAavVGiguLg688847we+VlpYGqqqqgl9PTk4G/vIv/zLwO7/zO4Hk5ORAdnZ24M/+7M8Cg4ODN37h88Sbb74ZkHTJ4+LzUlVVFSgtLb1kn4KCgoDVag3cfvvtgVdeeeWGr3u+ifR5Ki0tveJ4RN90/iz9X42NjYH8/PwbslZcHfdhAQAAxuMaFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPH+HyD974FaXxMgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#  must jettison some inHD map-boundary units to connect the enclave to the larger complement\n",
    "#For each HD to be triaged, define the list(s) of contiguous enclave units that are on the map boundary, \n",
    "#  and the in-HD map-boundary segments.  ID which in-HD MBS's adjoin one vs. two enclaves.\n",
    "#     Fill all internal enclaves, and all boundary enclaves < 0.05 aDP.\n",
    "#   Then each loop, look for boundary enclaves that can be filled without overpopping.\n",
    "#     If none, pick the 1/2 has-1-boundary-enclave-neighbor that is least painful to jettison to free an enclave.\n",
    "#       (Jettison score based on pop-to-Free and distance from HDcP)\n",
    "#         Update HDpop, HDlist, and enclave & HD-boundary associations, then re-loop\n",
    "# Later, we will swell-fill to square up pop (w/ checkEnclave) biasing toward close-to-hdCP, underused\n",
    "borderSet = set(borderUnits)\n",
    "debug, debugPlot = False, False\n",
    "startTime = time.time()  #takes about 1.5sec per HD triage\n",
    "clusterJettisonBoost = 3.  #this discourages jettisoning of underused clusters\n",
    "print(\"this code will solve large enclaves so HD and complement are contiguous for\",len(cantFill),\"HDs\")\n",
    "nEnclavesPerHD = [0 for t in range(nHDs)]\n",
    "HDenclaveLists = [list() for t in range(nHDs)]\n",
    "for iii,t in enumerate(cantFill):\n",
    "    if iii %20 == 0:\n",
    "        print(\"working on HD\",t,\"cantFill\",iii,\"of\",len(cantFill),\".Time is now\",int(time.time() - startTime) )\n",
    "    shedUs = list()    \n",
    "    enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "    nEnclavesPerHD[t] = len(enclaveLists)\n",
    "    HDenclaveLists[t] = enclaveLists.copy()\n",
    "    if debug:\n",
    "        print(\"pre-adjust HDpop for HD\",t,\"is\",HDvPop[t],\"I found\",len(enclaveLists),\"TOTAL enclaves\")\n",
    "    internalEnclaves, edgeEnclaves, combinedEnclBorderList = list(), list(), list()\n",
    "    for jjj, eL in enumerate(enclaveLists.copy() ):\n",
    "        enclaveBorderList = list ( set(eL).intersection(borderSet) )\n",
    "        if debug:\n",
    "            print(\"In enclave\",jjj,\"I found\",len(enclaveBorderList),\"units that were enclaved and are in the map borderSet\")\n",
    "        combinedEnclBorderList += enclaveBorderList\n",
    "        ePop = np.sum( [unitPop[u] for u in eL] )\n",
    "        if len(enclaveBorderList) == 0 or ePop < 0.05 * aDP:  #internal or small enclave.  Fill it\n",
    "            if ePop > 0:  #in a prev treatment, could be an empty (surrounded) unit that we don't care about\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += ePop\n",
    "            internalEnclaves.append(eL)\n",
    "        else:\n",
    "            edgeEnclaves.append(eL)\n",
    "    if debug:\n",
    "        print(\"Of these\",len(internalEnclaves),\"were internal or small, so I filled them, leaving\",\n",
    "              len(edgeEnclaves),\"non-small border = edge enclaves\" )\n",
    "    if debugPlot:\n",
    "        print(\"Here is the original HD, internal enclaves ('x') and non-small border enclaves by enclave no\")\n",
    "        plotPoly(hdCP[t].buffer(0.3))\n",
    "        for u in HDunitList[t]:\n",
    "            if unitPop[u] > 0:\n",
    "                plotPoly(unitGeom[u],0.2)\n",
    "        for L in internalEnclaves:\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u],1.5)\n",
    "                    plotCenter(\"x\",unitCP[u],8)\n",
    "        for L in edgeEnclaves: #############\n",
    "            for u in L:\n",
    "                if unitPop[u] > 0:\n",
    "                    plotPoly(unitGeom[u])\n",
    "                    #plotCenter(\"e\",unitCP[u],8)\n",
    "        #plotPoly(MAP,0.4)\n",
    "        #plt.show()\n",
    "    enclaveLists, combinedEnclBorderSet = list(), set(combinedEnclBorderList)\n",
    "    if len(edgeEnclaves) > 0:\n",
    "        # Now, we order by chain connectivity of HD and enclave sublists to be H0-E0-H1-E1 ... En-Hn+1\n",
    "        nonHDborderSet = borderSet.difference(  set(HDunitList[t]) )\n",
    "        nonHDlongBorderSet = nonHDborderSet.difference(combinedEnclBorderSet) #long=non HD boundary excluding enclaves\n",
    "        remainingEnclBorderSet = combinedEnclBorderSet.copy()\n",
    "        remainingHDborderSet =   borderSet.intersection(set(HDunitList[t]) )\n",
    "        #Now find the \"HDender\" unit which borders the non-enclave nonHD boundary, then find opp end of this HD bdry piece\n",
    "        HDender = list(set(getAdjoiners(nonHDlongBorderSet,unitNbrs)).intersection(remainingHDborderSet) )[0]\n",
    "        firstHDborderSet = getContigFromStarter(HDender,remainingHDborderSet,unitNbrs)\n",
    "        HDstarter = list(set(getAdjoiners(remainingEnclBorderSet,unitNbrs)).intersection(firstHDborderSet))[0]\n",
    "        HDborderSublists = [ list(firstHDborderSet) ]\n",
    "        unused = [1 for eL in edgeEnclaves]\n",
    "        eLadjoiners = [getAdjoiners(eL,unitNbrs) for eL in edgeEnclaves ]\n",
    "        for j in range(len(edgeEnclaves)): #find the enclave with the most HDstarter neighbors (at least 1)\n",
    "            found = False\n",
    "            for i,eL in enumerate(edgeEnclaves):\n",
    "                if unused[i] == 1:\n",
    "                    HDadjSet = set(HDborderSublists[j]).intersection(set(eLadjoiners[i]))\n",
    "                    if len(HDadjSet) > 0:\n",
    "                        unused[i] = 0\n",
    "                        eNo = i\n",
    "                        found = True\n",
    "                        remainingEnclBorderSet = remainingEnclBorderSet.difference(set(edgeEnclaves[eNo]) )\n",
    "                        enclaveLists.append(edgeEnclaves[eNo])\n",
    "                        remainingHDborderSet = remainingHDborderSet.difference(set(HDborderSublists[j]) )\n",
    "                        HDstarter = list(set(eLadjoiners[i]).intersection(remainingHDborderSet))[0]       \n",
    "                        HDborderSublists.append(getContigFromStarter(HDstarter,remainingHDborderSet,unitNbrs) )\n",
    "                        break\n",
    "                if found:\n",
    "                    break\n",
    "\n",
    "        enclavePops = [np.sum([unitPop[u] for u in L]) for L in enclaveLists]\n",
    "        #print(\"prior to adjustments, border enclavePops are\",enclavePops)         \n",
    "\n",
    "        nHDBS, nEnclaves = len(HDborderSublists), len(enclaveLists)\n",
    "        popToFree, freeingCounties, freeingUnits = [0. for n in range(nHDBS)], [list() for n in range(nHDBS) \n",
    "                                                                               ],[list() for n in range(nHDBS)]\n",
    "        for j,L in enumerate(HDborderSublists):\n",
    "            for u in L:\n",
    "                if int(allUnits[u]) == allUnits[u]:  #this border unit is a vtd\n",
    "                    c = countyNo[parentVTDno[allUnits[u]]]   #countyNo[allUnits[u]]\n",
    "                    if c not in freeingCounties[j]:\n",
    "                        if countyPop[c] < 0.1 * aDP:  #plan to add all in-county pop\n",
    "                            freeingCounties[j].append(c)\n",
    "                        else:\n",
    "                            popToFree[j] += unitPop[u]\n",
    "                            freeingUnits[j].append(u)\n",
    "                else: #border unit is a full county or cluster, or in a dense county.  Add the whole unit pop\n",
    "                    popToFree[j] += unitPop[u]\n",
    "                    freeingUnits[j].append(u)\n",
    "            for c in freeingCounties[j]:  #include ALL in-HD pop from each non-unit border county, not just its border units\n",
    "                #plotPoly(countyGeom[c],0.1)\n",
    "                for u in countyUnitList[c]:\n",
    "                    if u in HDunitList[t]:\n",
    "                        popToFree[j] += unitPop[u]\n",
    "                        freeingUnits[j].append(u)\n",
    "        freeingCP = [ getHDcp(  unitCP, unitPop, L) for L in freeingUnits ]\n",
    "        freeingScore = [ pTF/aDP - 0.5*freeingCP[j].distance(hdCP[t]) / avgDist[t] for j,pTF in enumerate(popToFree) ]\n",
    "        for j, fU in enumerate(freeingUnits):  #in above 0.5 is a fudgy\n",
    "            if len(barredJettisonSet.intersection(set(fU)) ) > 0: #has a cluster we want to hold onto\n",
    "                freeingScore[j] += clusterJettisonBoost #  ..so increase its score (make it harder to drop)\n",
    "        if debugPlot:\n",
    "            print(\"plotting the freeing units with negative numbers for each freeing group\")\n",
    "            for j,L in enumerate(freeingUnits):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        #plotPoly(unitGeom[u],0.5)\n",
    "                        plotCenter(\"-\"+str(j),unitGeom[u],6)\n",
    "                        pass\n",
    "            print(\"plotting the enclaveLists, with + numbers for each enclave\")\n",
    "            for j,L in enumerate(enclaveLists):\n",
    "                for u in L:\n",
    "                    if unitPop[u] > 0:\n",
    "                        plotPoly(unitGeom[u])\n",
    "                        plotCenter(j,unitCP[u],8)\n",
    "                        pass\n",
    "    \n",
    "    while len(enclaveLists) > 0:\n",
    "        if HDvPop[t] + np.min(enclavePops) < 1.05*aDP or np.min(enclavePops) < 0.05 * aDP:  #fill the smallest enclave\n",
    "            eNo = enclavePops.index(np.min(enclavePops))\n",
    "            HDunitList[t] += enclaveLists[eNo]\n",
    "            HDvPop[t] += enclavePops[eNo]\n",
    "            #print(t,\"=t. Absorbing enclave\",eNo,\"with pop\",enclavePops[eNo],\"HD total pop now\",int(HDfreePop[t]) )\n",
    "            enclaveBUs = list(set(enclaveLists[eNo]).intersection(set(borderUnits)) )\n",
    "            enclaveBpop = np.sum([unitPop[u] for u in enclaveBUs])\n",
    "            sNo, dropped_sNo = eNo, eNo+1\n",
    "            freeingScore[sNo] += freeingScore[sNo+1]+enclaveBpop/aDP\n",
    "            freeingUnits[sNo] += freeingUnits[sNo+1]+enclaveBUs\n",
    "            popToFree[sNo]    +=    popToFree[sNo+1]+enclaveBpop\n",
    "        else: #jettison one of the two end HD border lists; pick the less painful path to freedom\n",
    "            distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "            starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "            eNo, dropped_sNo = 0,0\n",
    "            if starterU not in freeingUnits[-1]:  #we can't drop the home unit, even to save an underused corner\n",
    "                if freeingScore[-1] < freeingScore[0] or starterU in freeingUnits[0]:\n",
    "                    eNo, dropped_sNo = len(enclaveLists)-1,len(enclaveLists)\n",
    "            barredIncluded0 = barredJettisonSet.intersection(set(freeingUnits[0]))\n",
    "            barredIncluded_1 = barredJettisonSet.intersection(set(freeingUnits[-1]))\n",
    "            #if len(barredIncluded0.union(barredIncluded_1)) > 0:\n",
    "            #    print(\"We are considering dropping an end unit to free from t\",t,\". Chosen, beg, end, scores are\")\n",
    "            #    print(dropped_sNo,barredIncluded0, barredIncluded_1, freeingScore[0], freeingScore[-1])\n",
    "            HDunitList[t] = list( set(HDunitList[t]).difference(set(freeingUnits[dropped_sNo]) ) )\n",
    "            HDvPop[t] -= popToFree[dropped_sNo]\n",
    "            #print(t,\"= t. Jettisoning HDborder\",dropped_sNo,\"with popToFree\",popToFree[dropped_sNo] )\n",
    "        del enclaveLists[eNo]\n",
    "        del enclavePops[eNo]\n",
    "        if debug:\n",
    "            print(t,\"= t. Now we have\",len(enclaveLists),\"left trapped.  Picked eNo, freeScores were\", eNo,freeingScore)\n",
    "        del freeingScore[dropped_sNo]\n",
    "        del freeingUnits[dropped_sNo]\n",
    "        del popToFree   [dropped_sNo] \n",
    "\n",
    "    #plotPoly(MAP,0.4)\n",
    "    if debugPlot:\n",
    "        plt.show()    \n",
    "        \n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t]*nDistricts\n",
    "print(\"after the MustFree code run, we have the following for cluster usage\")\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ca9ce3cb-c150-4f39-ae55-f781d39d7f57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "postMustFreeUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c32dd108-073b-48aa-99ce-fea50c8d023c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t] for t in popHDlist] )\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f58fe2d0-3c62-412f-8984-ff5d67ea9b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After above work, re-classification of contiguity problems?\n",
      "working on HD 0\n",
      "working on HD 500\n",
      "working on HD 1000\n",
      "working on HD 2000\n",
      "working on HD 2500\n",
      "working on HD 4000\n",
      "working on HD 4500\n",
      "working on HD 5000\n",
      "working on HD 5500\n",
      "working on HD 9000\n",
      "working on HD 9500\n",
      "working on HD 10000\n",
      "working on HD 14000\n",
      "out of 6033 total HDs, there were 5747 148 0 138 HDs that were clean, discontig only, enclave-only, both problems after triage\n"
     ]
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"After above work, re-classification of contiguity problems?\")\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1ef683a2-4429-4a91-b1ea-a9b0950dea22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on current HD diam for HD number 0\n",
      "working on current HD diam for HD number 801\n",
      "working on current HD diam for HD number 2098\n",
      "working on current HD diam for HD number 3363\n",
      "working on current HD diam for HD number 4848\n",
      "working on current HD diam for HD number 7345\n",
      "working on current HD diam for HD number 9331\n",
      "working on current HD diam for HD number 13048\n",
      "here is the histogram of HD diameter = sqrt(area)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HDdiam = [0. for t in range(nHDs)]\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on current HD diam for HD number\",t)\n",
    "    HDdiam[t] = (HDpoly[t].intersection(MAP) ).area ** 0.5\n",
    "plt.hist([HDdiam[t] for t in popHDlist])\n",
    "print(\"here is the histogram of HD diameter = sqrt(area)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "45e3c17c-8c16-4bc5-b7fc-25b9250ed11a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we will regrow all disco'd HDs off by more than  38876 but less than 194379\n",
      "... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\n",
      "This is a total of 286 HDs to triage in this block\n",
      "working on swell-filling discontig HD 3 our 1 th HD we think we can swell out of 286 0.0 sec elapsed\n",
      "working on swell-filling discontig HD 2072 our 41 th HD we think we can swell out of 286 1241.161 sec elapsed\n",
      "working on swell-filling discontig HD 3828 our 81 th HD we think we can swell out of 286 1630.399 sec elapsed\n",
      "working on swell-filling discontig HD 5274 our 121 th HD we think we can swell out of 286 2362.763 sec elapsed\n",
      "working on swell-filling discontig HD 427 our 161 th HD we think we can swell out of 286 2976.259 sec elapsed\n",
      "working on swell-filling discontig HD 3769 our 201 th HD we think we can swell out of 286 5111.047 sec elapsed\n",
      "working on swell-filling discontig HD 5188 our 241 th HD we think we can swell out of 286 5851.066 sec elapsed\n",
      "working on swell-filling discontig HD 9757 our 281 th HD we think we can swell out of 286 7215.62 sec elapsed\n",
      "we partially regrew 244 HDs, leaving 42 to regrow from scratch\n"
     ]
    }
   ],
   "source": [
    "### THIS IS THE CANSWELL CODE\n",
    "print(\"Here, we will regrow all disco'd HDs off by more than \",int(aDP-minPostFixPop),\"but less than\",int(0.25*aDP) )\n",
    "print(\"... from the contiguous block that contains the hdCP (not full regrowth).  We will swell out, avoiding enclaves\")\n",
    "HDnAddedUnits = [0 for t in range(nHDs)]\n",
    "maxGap = 0.9 * np.median(unitPop)  #set a reasonable gap prior to picking the last block\n",
    "HDdiam = [HDpoly[t].area ** 0.5 for t in range(nHDs) ] #in case not defined before\n",
    "badDiscoList = list()\n",
    "nCanSwell = 0\n",
    "triageList = discontigOnly + bothProblems\n",
    "print(\"This is a total of\",len(triageList),\"HDs to triage in this block\")\n",
    "startTime = time.time()\n",
    " \n",
    "for i,t in enumerate(triageList): #canSwell\n",
    "    if i%40 == 0:\n",
    "        SEC = r3(time.time()-startTime)\n",
    "        print(\"working on swell-filling discontig HD\",t,\"our\",i+1,\"th HD we think we can swell out of\",len(triageList),SEC,\"sec elapsed\" )\n",
    "    distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "    starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = getContigFromStarter(starterU, HDunitList[t], unitNbrs)\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    if contigPop < 0.50 * aDP or contigPop > 2.*aDP - minPostFixPop:  #formerly < 0.75 aDP\n",
    "        badDiscoList.append(t)\n",
    "    else: #rebuild from this big piece\n",
    "        nCanSwell +=1\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "        nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "        nearHDscore = [ (unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] for uu in adjoiners ] \n",
    "        stillGoing = True\n",
    "\n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)  #so add it to our growing HD ...\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append((unitUse[uu] - 1.) + 0.5*(unitCP[uu].distance(\n",
    "                            HDpoly[t]) + unitCP[uu].distance(hdCP[t]) ) / HDdiam[t] )\n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "        for u in addedList:\n",
    "            unitUse[u] += HDweight[t] * nDistricts\n",
    "        for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "            unitUse[u] -= HDweight[t] * nDistricts       \n",
    "        HDunitList[t] = contigUs + addedList\n",
    "        HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "        HDnAddedUnits[t] = len(addedList)\n",
    "    \n",
    "badDiscoList += enclaveOnly\n",
    "print(\"we partially regrew\",nCanSwell,\"HDs, leaving\",len(badDiscoList),\"to regrow from scratch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f57325dd-523c-466a-80cb-8e10b739b4ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous avg use and its sd are 1.00563 0.14822\n",
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00552 0.1403\n",
      "CCB cluster 0 = unit 5839 with pop 127657.0 now has use of 0.9994598188850052\n",
      "CCB cluster 1 = unit 5840 with pop 164779.0 now has use of 0.9819476615945295\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"previous avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "\n",
    "for u, unitNo in enumerate(allUnits):\n",
    "    if unitNo % 1 == 0.25:\n",
    "        print(\"CCB cluster\",int(unitNo),\"= unit\",u,\"with pop\",unitPop[u],\"now has use of\",unitUse[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0ce918e1-0d30-4930-9fb8-0769d793683c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 1001 time is now 10\n",
      "working on HD 2760 time is now 19\n",
      "working on HD 4647 time is now 33\n",
      "working on HD 7345 time is now 46\n",
      "working on HD 9531 time is now 56\n",
      "working on HD 14158 time is now 65\n",
      "out of 6033 total HDs, there were 5991.0 contiguous and 5982.0 complement-contiguous HDs\n",
      "28 HDs had both discontiguity problems, while enclave-only = 23 and discontig only= 14\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 42 23\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THIS is the FINDSTILLBROKEN code\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%1000 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "463db75a-694e-4c7d-9693-e10f3b4d65db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before addressing enclaves, let's triage the discontinuous HDs\n",
      "Now work on dropping smallish disconnected pieces that would keep us above 738641 district pop vs 777517 target\n",
      "we'll also trim any HD with total islands' pop <= 15550\n",
      "working on HD 2485\n",
      "Out of 42 discontig HDs 42 had discontig HDs.\n",
      "Of these, 1 won't be under 738641 after all minor discontigys were shed, while 41 would be too underpopped if we trimmed the discontig pieces\n",
      "here is adjusted pop by original pop for those we trimmed and those we didn't (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, reclassify after the trimming\n",
      "There are 41 HDs still with both discontinuity problems\n",
      "Additionally, 0 of the originally discontig HDs are now contig but still have an enclave\n"
     ]
    }
   ],
   "source": [
    "print(\"Before addressing enclaves, let's triage the discontinuous HDs\")\n",
    "#this is the CANTRIM code####\n",
    "\n",
    "#for t in sPgenerator:\n",
    "#    isContig, smallP = isContiguous(filledHDvtdList[t],unitNbrs)\n",
    "#    if isContig:\n",
    "#        print(\"oops!\",t)\n",
    "maxNudgeDownPop = int(0.02 * aDP)\n",
    "minPostFixPop = int(0.95 * aDP)\n",
    "print(\"Now work on dropping smallish disconnected pieces that would keep us above\",minPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "print(\"we'll also trim any HD with total islands' pop <=\",maxNudgeDownPop)\n",
    "nOrigIslands = [0 for t in range(nHDs)]\n",
    "totalIslandPop = [0. for t in range(nHDs)]\n",
    "tryToTrim, canTrim, cantTrim = list(), list(), list()\n",
    "for jj, t in enumerate(sPgenerator):\n",
    "    if jj%100 == 0:\n",
    "        print(\"working on HD\",t)\n",
    "    currList, shedList, shedPop = HDunitList[t].copy(), list(),  0.\n",
    "    done,newList = isContiguous(currList,unitNbrs)\n",
    "    if not done:\n",
    "        tryToTrim.append(t)\n",
    "        while not done:\n",
    "            shedList += newList\n",
    "            shedPop += np.sum( [unitPop[u] for u in newList] )\n",
    "            currList = list (  set(currList).difference(set(newList ))  )\n",
    "            done, newList = isContiguous(currList, unitNbrs)\n",
    "            nOrigIslands[t] +=1\n",
    "            \n",
    "        totalIslandPop[t] = shedPop            \n",
    "        if shedPop <= maxNudgeDownPop or HDvPop[t] - shedPop >= minPostFixPop:\n",
    "            canTrim.append(t)\n",
    "            HDvPop[t] -= shedPop\n",
    "            HDunitList[t] = list (set(HDunitList[t]).difference(set(shedList)) )\n",
    "        else:\n",
    "            cantTrim.append(t)\n",
    "print(\"Out of\",len(sPgenerator),\"discontig HDs\",len(tryToTrim),\"had discontig HDs.\")\n",
    "print(\"Of these,\",len(canTrim),\"won't be under\",minPostFixPop,\"after all minor discontigys were shed, while\",\n",
    "      len(cantTrim),\"would be too underpopped if we trimmed the discontig pieces\")\n",
    "plt.scatter([HDvPop[t] + totalIslandPop[t] for t in canTrim],[HDvPop[t] for t in canTrim])\n",
    "plt.scatter([HDvPop[t]                     for t in cantTrim],[HDvPop[t] for t in cantTrim],marker=\"x\")\n",
    "print(\"here is adjusted pop by original pop for those we trimmed and those we didn't (x)\")\n",
    "plt.plot([0.9*aDP, 1.1*aDP],[aDP, aDP], ls=\"--\")\n",
    "plt.show()\n",
    "\n",
    "print(\"Now, reclassify after the trimming\")\n",
    "badDiscoList, stillHasEnclave = list(), list()\n",
    "for t in sPgenerator:    \n",
    "    unbroken, noEnclave, __, ____ = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if not unbroken:\n",
    "        badDiscoList.append(t)  #will do a major triage on these later\n",
    "    if unbroken and not noEnclave:\n",
    "        stillHasEnclave.append(t)\n",
    "print(\"There are\",len(badDiscoList),\"HDs still with both discontinuity problems\")\n",
    "print(\"Additionally,\",len(stillHasEnclave),\"of the originally discontig HDs are now contig but still have an enclave\")\n",
    "eLgenerator += stillHasEnclave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "39d37c15-759f-462c-b503-047b6b5e1554",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77 447 21\n",
      "{6155, 6162, 21, 6170, 36, 4132, 4136, 41, 4148, 4155, 4089, 97, 2147, 4217, 4232, 4238, 2206, 164, 6322, 178, 6327, 4284, 4301, 227, 4324, 238, 251, 2300, 2301, 255, 2303, 2304, 2305, 2306, 2307, 4352, 4355, 269, 274, 275, 2323, 2326, 2327, 2329, 2332, 284, 2333, 288, 2341, 2344, 2345, 302, 306, 2358, 2359, 4406, 2360, 321, 2372, 2373, 2374, 2375, 2376, 2381, 336, 2385, 2387, 2388, 2403, 2404, 2412, 2413, 370, 382, 390, 398, 411, 4510, 4515, 4518, 4519, 4535, 4536, 4547, 4548, 4553, 458, 4564, 474, 4577, 4581, 494, 511, 4612, 528, 535, 538, 4639, 545, 562, 563, 570, 573, 4678, 4697, 4698, 4699, 4702, 4703, 610, 615, 4712, 4716, 621, 628, 4733, 643, 4741, 4742, 648, 4747, 4748, 663, 669, 678, 694, 4795, 4798, 4799, 4800, 4801, 4802, 4803, 4804, 4805, 4806, 711, 4807, 4808, 4809, 4813, 720, 4816, 722, 723, 724, 725, 728, 729, 4827, 733, 4833, 738, 744, 745, 746, 747, 748, 750, 754, 756, 762, 2813, 4866, 4867, 783, 2836, 2838, 2843, 4891, 2844, 797, 804, 2854, 2855, 808, 4905, 2856, 2857, 811, 4904, 813, 2861, 2866, 819, 2871, 824, 825, 827, 829, 2879, 833, 2882, 835, 2885, 4936, 841, 846, 848, 849, 852, 855, 4954, 859, 869, 870, 876, 880, 881, 4977, 883, 4980, 4981, 886, 4984, 4985, 893, 4989, 896, 897, 901, 902, 4997, 4998, 4999, 906, 5002, 908, 913, 914, 917, 921, 5022, 932, 934, 941, 945, 5046, 959, 961, 5057, 964, 5060, 965, 5061, 5062, 969, 970, 5068, 5071, 5072, 988, 989, 5088, 998, 999, 5098, 1004, 5100, 5101, 1013, 1015, 5113, 1025, 5123, 1029, 1038, 1040, 5141, 1047, 1051, 1067, 1070, 1076, 1081, 1083, 5179, 1089, 5186, 5187, 1095, 1096, 1097, 1099, 5195, 5196, 5197, 5199, 5200, 1107, 1110, 1112, 1119, 1131, 5228, 5229, 5231, 1136, 5232, 5234, 5235, 1140, 5237, 1145, 1148, 5245, 1151, 5247, 5258, 5260, 1168, 1169, 1171, 5272, 5273, 5282, 5283, 5284, 5285, 5291, 5295, 5296, 5302, 5303, 5305, 5306, 5307, 5308, 5313, 1218, 5314, 1220, 5315, 5322, 5323, 5329, 5330, 5332, 5338, 5339, 5342, 5344, 5349, 5353, 5354, 5359, 5366, 5372, 5373, 3326, 5376, 5377, 5378, 5379, 5388, 5391, 3345, 1298, 3347, 5396, 3349, 1303, 1304, 5399, 1312, 1337, 1339, 5443, 5445, 1350, 1351, 5447, 1353, 1354, 1355, 1358, 5454, 1360, 5455, 5459, 1365, 5463, 5468, 1374, 5473, 1378, 1379, 5474, 5476, 5478, 1388, 1399, 1400, 1405, 1409, 1410, 1414, 1420, 1421, 1422, 1425, 1433, 5574, 1482, 1492, 1500, 1503, 1507, 5605, 1510, 1511, 1512, 1515, 1517, 1519, 1522, 1527, 1544, 1545, 1546, 1551, 5648, 1559, 1573, 1574, 1577, 1578, 1579, 1580, 1586, 1587, 1589, 1590, 1592, 5688, 1594, 1595, 3650, 5698, 3653, 1612, 5709, 3664, 3665, 1618, 3666, 5716, 1625, 3674, 3676, 5725, 3678, 3679, 3685, 5735, 3688, 5744, 5747, 3702, 3703, 3705, 3714, 3721, 1678, 1679, 5775, 5778, 3731, 3733, 3735, 5786, 3743, 3744, 5797, 5803, 1713, 1714, 5836, 1742, 5842, 5844, 5845, 1753, 5850, 5855, 1763, 3819, 3824, 3827, 5883, 5884, 5886, 1793, 1804, 5943, 5951, 5955, 6004, 6064, 6080, 6130, 6133, 2041}\n"
     ]
    }
   ],
   "source": [
    "print(len(discontigOnly),len(bothProblems), len(badDiscoList))\n",
    "print((set(discontigOnly).union(set(bothProblems)) ) .difference(set(badDiscoList)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2e54bac4-c327-473d-bbb4-257486bddd20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 41\n",
      "swell-generating HD 2485 our 1 th HD we must regrow out of 41 0 sec elapsed\n",
      "swell-generating HD 3318 our 11 th HD we must regrow out of 41 788 sec elapsed\n",
      "swell-generating HD 3982 our 21 th HD we must regrow out of 41 2325 sec elapsed\n",
      "swell-generating HD 9553 our 31 th HD we must regrow out of 41 3067 sec elapsed\n",
      "swell-generating HD 9910 our 41 th HD we must regrow out of 41 3157 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "#THIS IS THE MUSTSPAWN code block\n",
    "print(\"And now, the major disco'd HDs (< 0.75 aDP in HDcP-contig portion and/or enclaves).  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = getAdjoiners(currList, unitNbrs)\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDpoly[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b78199da-ac7c-4cfb-ac0b-26363a8a8c56",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prev avg use and its sd are 1.00552 0.1403\n",
      "defining and displaying current unit use after most recent manipulations; compare to original farther above.\n",
      "current avg use and its sd are 1.00542 0.13829\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"prev avg use and its sd are\",r5(currAvg),r5(currSD) )\n",
    "print(\"defining and displaying current unit use after most recent manipulations; compare to original farther above.\")\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.1:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a2242181-3a07-40b7-ae56-bbe47bd2dc79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(not-so-)final check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 501 time is now 8 sec\n",
      "working on HD 1001 time is now 16 sec\n",
      "working on HD 1778 time is now 22 sec\n",
      "working on HD 2760 time is now 28 sec\n",
      "working on HD 3730 time is now 40 sec\n",
      "working on HD 4647 time is now 49 sec\n",
      "working on HD 5148 time is now 62 sec\n",
      "working on HD 7345 time is now 79 sec\n",
      "working on HD 9029 time is now 88 sec\n",
      "working on HD 9531 time is now 96 sec\n",
      "working on HD 10035 time is now 102 sec\n",
      "working on HD 14158 time is now 107 sec\n",
      "out of 6033 total HDs, there were 6010 0 23 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 108 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"(not-so-)final check -- any more disco's ?\")\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "623fb818-67d9-458d-b854-4c70dfa449ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 816392 district pop vs 777517 target\n",
      "working on enclave-y HD 109 . We have evaluated 0 of 4776 enclavy HDs.Time is now 0\n",
      "Out of 4776 HDs with enclaves 23 had enclaves.\n",
      "Of these, 23 wouldn't be over 816392 if all enclaves filled, while 0 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OPTIONAL if still enclaves -- rerun the CANFILL CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.05 * aDP)\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",maxPostFixPop,\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(enclaveOnly):  #internals + edgers):\n",
    "    if iii%100 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(internals+edgers),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(internals+edgers),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "18f46c40-7f28-4714-962f-0b462ff97b8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final ??? check -- any more disco's ?\n",
      "working on HD 0 time is now 0 sec\n",
      "working on HD 501 time is now 4 sec\n",
      "working on HD 1001 time is now 9 sec\n",
      "working on HD 1778 time is now 13 sec\n",
      "working on HD 2760 time is now 17 sec\n",
      "working on HD 3730 time is now 22 sec\n",
      "working on HD 4647 time is now 28 sec\n",
      "working on HD 5148 time is now 33 sec\n",
      "working on HD 7345 time is now 38 sec\n",
      "working on HD 9029 time is now 42 sec\n",
      "working on HD 9531 time is now 48 sec\n",
      "working on HD 10035 time is now 52 sec\n",
      "working on HD 14158 time is now 57 sec\n",
      "out of 6033 total HDs, there were 6033 0 0 0 HDs that were clean, discontig only, enclave-only, both problems after triage\n",
      "this took 57 seconds with maxLoop =  5\n"
     ]
    }
   ],
   "source": [
    "print(\"final ??? check -- any more disco's ?\")  #run again if we ran above block to fill minor enclaves\n",
    "maxLOOP = 5  #can increase to avoid false enclave detection\n",
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%500 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime),\"sec\")\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs, maxLOOP)\n",
    "    if not noEnclave:\n",
    "        noEnclave, enclaveList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems after triage\")\n",
    "print(\"this took\",int(time.time() - startTime),\"seconds with maxLoop = \", maxLOOP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "de4d8af8-cc64-4215-9b95-f4c6a2bd03af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "savedAgainUnitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f4bb76c7-0ae0-40cb-9c6a-ee436dfc30c4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "HDunitList = [savedAgainUnitList[t].copy() for t in range(nHDs)]  #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f3bcd6a8-40b6-4764-a0d1-6814e3286cb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking on our corner clusters and unit county usage\n",
      "usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\n",
      "1.19574     0     5107 0.5\n",
      "0.97225     5839     127657 0.25\n",
      "0.97639     5840     164779 0.25\n"
     ]
    }
   ],
   "source": [
    "print(\"checking on our corner clusters and unit county usage\")\n",
    "print(\"usage, unit no, unit pop, type (0.5 = unit county, 0.25 = cluster) ...\")\n",
    "for u in range(nUnits):\n",
    "    if int(allUnits[u]) != allUnits[u] :\n",
    "        print(r5(unitUse[u]),\"   \",u,\"   \",int(unitPop[u]), allUnits[u]%1 )\n",
    "# note for MN, option 3 here led to tight CCB unit usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1e983c03-777a-4e81-bed0-34a620175f83",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9bc31b5a-992d-4a38-b9fe-3470b55e1e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigButOffPopUnit.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7b9b68ef-6ae9-4d80-895d-41a86f48a55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(\"hi\")  #see MN for code to boost any under-used CCBs via border linking strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6150cac-2249-415b-a364-edf55dc441ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "114705bb-9640-45f1-9f25-030030efaa63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below here -- working on tightening use distro while squaring up pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5d7d8519-eb3d-4345-9ac6-ab888e61c348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00543 0.13829\n"
     ]
    }
   ],
   "source": [
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b1971853-bda7-49a4-9f25-1bdfd5e75aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 801\n",
      "working on avg unit-to-HDcp dist for HD 2098\n",
      "working on avg unit-to-HDcp dist for HD 3363\n",
      "working on avg unit-to-HDcp dist for HD 4848\n",
      "working on avg unit-to-HDcp dist for HD 7345\n",
      "working on avg unit-to-HDcp dist for HD 9331\n",
      "working on avg unit-to-HDcp dist for HD 13048\n"
     ]
    }
   ],
   "source": [
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f5b99900-614a-47df-b7de-5d59fffd6d3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before patching, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 1.00543 0.13829\n"
     ]
    }
   ],
   "source": [
    "#Restart - may want to comment out first line\n",
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)]\n",
    "unitUse = [0. for u in range(nUnits) ]\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, weights = unitPop,bins = 50)\n",
    "plt.show()\n",
    "plt.hist([HDvPop[t] for t in popHDlist], bins=50)\n",
    "plt.show()\n",
    "print(\"before patching, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "27b51cd0-8bb6-4b16-bb79-37498e59ce5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 410 HDs with pop < 769741 to within 1016\n",
      "working on squaring up HD 0 time is now 0\n",
      "working on squaring up HD 2492 time is now 94\n",
      "working on squaring up HD 5226 time is now 388\n",
      "working on squaring up HD 9348 time is now 639\n",
      "working on squaring up HD 14045 time is now 727\n",
      "We have addressed underpop in a total of 410 HDs\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList = list()\n",
    "minDistrictPop, maxDistrictPop = 0.99 * aDP, 1.01 * aDP\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "print(\"We have addressed underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.axhline(y=aDP, xmin = 0.9*aDP, xmax = 1.1*aDP, ls=\"--\")\n",
    "plt.axvline(x=aDP, ymin = 0.9*aDP, ymax = 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bc01409d-d9e4-43a6-9fae-c76aa7bdc855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 1.00543 0.13829\n",
      "amped unit avg and sd usage are 1.00829 0.13641\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ee512b2b-4daa-4a6b-9452-e8dfdfdebc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the current barredJettisonSet (usually CCB's) is {5840, 5839}\n"
     ]
    }
   ],
   "source": [
    "#CHECK ON BARRED JETTISON SET BEFORE RUNNING BELOW\n",
    "print(\"the current barredJettisonSet (usually CCB's) is\",barredJettisonSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "dd7d9818-344c-4150-917b-57a4ee051c92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 603 overPopped HDs to within 1016\n",
      "working on squaring down HD 47 time is now 0\n",
      "working on squaring down HD 658 time is now 55\n",
      "working on squaring down HD 1345 time is now 122\n",
      "working on squaring down HD 2344 time is now 142\n",
      "working on squaring down HD 3044 time is now 225\n",
      "working on squaring down HD 4399 time is now 253\n",
      "working on squaring down HD 4940 time is now 308\n",
      "working on squaring down HD 5331 time is now 341\n",
      "working on squaring down HD 9225 time is now 395\n",
      "working on squaring down HD 9841 time is now 450\n",
      "working on squaring down HD 14174 time is now 487\n",
      "We have addressed overpop in a total of 603 HDs\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "#HDunitList = [ampedHDlist[t].copy() for t in range(nHDs)]\n",
    "#HDvPop =     [ampedHDpop[t]         for t in range(nHDs)]\n",
    "#unitUse =    [ampedUnitUse[u]       for u in range(nUnits)] #saving in case I need to re-run\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList = list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = 0.9 * np.median(unitPop)   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%60 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        #avgDist = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = HDunitList[t][distList.index(np.min(distList))]\n",
    "        barredSet = barredJettisonSet  #new 4/10/24 for MN, ban CCB jettisoning from ALL HD's, not just near ones\n",
    "        #barredSet = set(get2nbrs([starterU],unitNbrs)).union(barredJettisonSet)  #weaker specification - must be close to be barred\n",
    "\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and\n",
    "                                                                      u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )\n",
    "\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "\n",
    "print(\"We have addressed overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axhline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b02c7bca-1eaa-445b-bd8f-97c161794ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9a19b23e-43cc-4609-9ee7-6d1057a7a9e1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the stats prior to any equi-pop patching\n",
      "amped unit avg and sd usage are 1.00829 0.13641\n",
      "shed unit avg and sd usage are 1.00068 0.13075\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here are the final populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 14191\n",
      "working on HD 300 out of 14191\n",
      "working on HD 601 out of 14191\n",
      "working on HD 901 out of 14191\n",
      "working on HD 1201 out of 14191\n",
      "working on HD 1778 out of 14191\n",
      "working on HD 2450 out of 14191\n",
      "working on HD 2925 out of 14191\n",
      "working on HD 3363 out of 14191\n",
      "working on HD 4346 out of 14191\n",
      "working on HD 4647 out of 14191\n",
      "working on HD 4948 out of 14191\n",
      "working on HD 5252 out of 14191\n",
      "working on HD 5554 out of 14191\n",
      "working on HD 8725 out of 14191\n",
      "working on HD 9029 out of 14191\n",
      "working on HD 9331 out of 14191\n",
      "working on HD 9633 out of 14191\n",
      "working on HD 9935 out of 14191\n",
      "working on HD 13156 out of 14191\n",
      "working on HD 14158 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n",
      "underpop contigFail, enclaveFail = 0 0 out of 410\n",
      "overpop contigFail, enclaveFail = 0 0 out of 603\n",
      "total contigFail, enclaveFail =  0 0\n",
      "CCB unit 5839 with pop 127657.0 now has use 0.97225\n",
      "CCB unit 5840 with pop 164779.0 now has use 0.97639\n"
     ]
    }
   ],
   "source": [
    "print(\"Here are the stats prior to any equi-pop patching\")\n",
    "shedUnitUse = [0. for u in range(nUnits)]\n",
    "shedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "shedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in shedHDlist[t]:\n",
    "        shedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "#plt.hist(latestUnitUse,bins=50,weights=unitPop,label=\"pre-squaring\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.hist(shedUnitUse, bins=50, weights=unitPop,label=\"shed\",histtype=\"step\")\n",
    "plt.legend()\n",
    "#latestAvg, latestSD = getWeightedAvgAndSD(latestUnitUse,unitPop)\n",
    "shedUnitUseAvg, shedUnitUseSD = getWeightedAvgAndSD(shedUnitUse,unitPop)\n",
    "#print(\"orig unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "print(\"shed unit avg and sd usage are\", r5(shedUnitUseAvg),  r5(shedUnitUseSD) )\n",
    "plt.show()\n",
    "# note: when I ran this early Jan, went from 0.12662 to 0.09961 to 0.09432 SD orig-amped-shed, but contig not yet done\n",
    "print(\"and here are the final populations\")\n",
    "plt.hist([shedHDpop[t] for t in popHDlist],bins=20)\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "failEnclaveSet, failContigSet = set(), set()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs,5)\n",
    "    if not noEnclave:\n",
    "        noEnclave, eList = isContiguous(list({u for u in range(nUnits)}.difference(set(HDunitList[t]))),unitNbrs)\n",
    "    if not unbroken or not noEnclave:\n",
    "        #print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "        pass\n",
    "    if not unbroken:\n",
    "        failContigSet.add(t)\n",
    "    if not noEnclave:\n",
    "        failEnclaveSet.add(t)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")\n",
    "failContigUnderpopped, failEnclaveUnderpopped = set(underPoppedList).intersection(failContigSet), set(underPoppedList).intersection(failEnclaveSet)\n",
    "failContigOverpopped, failEnclaveOverpopped = set(overPoppedList).intersection(failContigSet), set(overPoppedList).intersection(failEnclaveSet)\n",
    "print(\"underpop contigFail, enclaveFail =\",len(failContigUnderpopped), len(failEnclaveUnderpopped),\"out of\",len(underPoppedList))\n",
    "print(\"overpop contigFail, enclaveFail =\",len(failContigOverpopped), len(failEnclaveOverpopped),\"out of\",len(overPoppedList))\n",
    "print(\"total contigFail, enclaveFail = \",len(failContigSet), len(failEnclaveSet) )\n",
    "for u in range(nUnits):\n",
    "    if abs( allUnits[u]%1 - 0.25) < 0.01:\n",
    "        print(\"CCB unit\",u,\"with pop\",unitPop[u],\"now has use\",r5(unitUse[u]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ff3288f1-f9ca-473c-886c-ee8d6c2637bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14191 HDs, non-empty HDs 6033 5841 units\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, \"HDs, non-empty HDs\",len(popHDlist), nUnits,\"units\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d27207a-8e8d-40f2-a624-62ea7ba21920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see MD code if we need to remove a stuck CCB's from any HDs and then redo the square-up after dropping them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e7bfa674-8fc8-42c8-86e9-76d334d3c47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"opt3ContigGoodPop.csv\" #\"contigUnpatchedB.csv\" unpatchedContigGoodPop.csv\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "c0c3a3d8-cf79-40ad-aa2e-b6793266f1d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prior to patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    }
   ],
   "source": [
    "#SKIP THIS FOR STATES WITH FRAGMENTED VTD'S ###########\n",
    "print(\"prior to patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist = [list() for u in range(nUnits)]\n",
    "for u in range(nUnits):\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        unitVTDlist[u] = [allUnits[u]]\n",
    "        for i,uu in enumerate(surrounders):\n",
    "            if u == uu:\n",
    "                unitVTDlist[u].append(surroundedVTDs[i])\n",
    "\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        vtdList[t] += unitVTDlist[u]\n",
    "    #add more stuff here to convert to vtd lists\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"needsEnclaveRemoval.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a36c0ba-5a68-445b-a223-b7fa55dfd728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 4110 HD centers\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(vtdGeom)\n",
    "print(\"there are\",nHDs,\"HD centers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1b794580-5a58-4238-b310-7c87872ed679",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\n",
      "Must have already established unitGeoms, pops, topology -- or read in via next block\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./2024state_HD_output/NYupper14191opt3ContigGoodPop.csv ./2024state_HD_output/NYupper14191opt3ContigGoodPop.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of HDweight should be unity, actually is 0.999999999999799\n",
      "I will now renormalize\n",
      "normalized weight is now 1.0\n",
      "I read in 14191 HD lists of units\n",
      "orig unit avg and sd usage are 1.00068 0.13075\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"this optional RESTART block pulls in an existing UNIT (not vtd) list for patching\") #vtdlist for patching\")\n",
    "print(\"Must have already established unitGeoms, pops, topology -- or read in via next block\")\n",
    "guessedFile = \"enter the unpatched UNIT list file, e.g. ./2024state_HD_output/\"+STATE+str(nHDs)+\"opt3ContigGoodPop.csv\"\n",
    "infile = input(guessedFile)\n",
    "inDF = pd.read_csv(infile)\n",
    "vtdListString = inDF[\"HDunitList\"]  #inDF[\"HDvtdList\"]\n",
    "nHDs = len(vtdListString)\n",
    "inVTDlist = [ast.literal_eval(vtdListString[t]) for t in range(nHDs)]\n",
    "HDweight = inDF[\"HDweight\"]\n",
    "sumWt = np.sum(HDweight)\n",
    "print(\"sum of HDweight should be unity, actually is\",sumWt)\n",
    "print(\"I will now renormalize\")\n",
    "HDweight = [HDweight[t] /sumWt for t in range(nHDs)]\n",
    "print(\"normalized weight is now\",np.sum(HDweight))\n",
    "HDvPop = inDF[\"HDvPop\"].to_list()\n",
    "hdCPx, hdCPy = inDF[\"centroid x\"], inDF[\"centroid y\"]\n",
    "hdCP = [Point(hdCPx[t], hdCPy[t]) for t in range(nHDs)]\n",
    "print(\"I read in\",nHDs,\"HD lists of units\") #vtds\")\n",
    "HDunitList = [inVTDlist[t].copy() for t in range(nHDs)]\n",
    "#HDunitList = [list() for t in range(nHDs)]\n",
    "#for t in range(nHDs):\n",
    "#    if t%500 == 0:\n",
    "#        print(\"working on converting HD\",t,\"from vtd list to unit list\")\n",
    "#    for v in inVTDlist[t]:  #this will skip over surrounded units, whose pops we have added to their surrounders\n",
    "#        if v in allUnits:\n",
    "#            HDunitList[t].append(allUnits.index(v))\n",
    "#    for c in unitCounties:\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(c+0.5)\n",
    "#            HDunitList[t].append(u)\n",
    "#    for j, L in enumerate(CCBlist):\n",
    "#        c = L[0]\n",
    "#        if countyTractList[c][0] in inVTDlist[t]:\n",
    "#            u = allUnits.index(j+0.25)\n",
    "#            HDunitList[t].append(u) \n",
    "            \n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(unitUse, bins=50, weights=unitPop,label=\"read-in\",histtype=\"step\")\n",
    "plt.legend()\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"orig unit avg and sd usage are\",r5(unpatchedAvg), r5(unpatchedSD) )\n",
    "plt.show()\n",
    "currAvg, currSD = unpatchedAvg, unpatchedSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8014f0bf-74bb-40bc-b217-af4e204ea9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upon restart, need to pull in unit topology and data\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv ./state_map_files/MNunitTopologies_06Apr24.csv\n"
     ]
    }
   ],
   "source": [
    "print(\"upon restart, need to pull in unit topology and data\")  #note, this won't have geometries for plotting, but fine for patching\n",
    "topologyFile = \"enter the unpatched UNIT list file, e.g. ./state_map_files/MNunitTopologies_06Apr24.csv\"\n",
    "infile = input(topologyFile)\n",
    "topDF = pd.read_csv(infile)\n",
    "unitCPx, unitCPy, unitPop = topDF[\"centroid x\"], topDF[\"centroid y\"], topDF[\"unitPop\"]\n",
    "nUnits = len(unitPop)\n",
    "unitCP = [Point(unitCPx[u], unitCPy[u]) for u in range(nUnits) ]\n",
    "onBorder = topDF[\"onBorder\"]\n",
    "borderSet = set()\n",
    "for i, onB in enumerate(onBorder):\n",
    "    if onB == 1:\n",
    "        borderSet.add(i)\n",
    "borderList = list(borderSet)\n",
    "\n",
    "nbrListString = topDF[\"neighborList\"]\n",
    "unitNbrs = [ast.literal_eval(nbrListString[u]) for u in range(nUnits)]\n",
    "unitParentVTDno = topDF[\"unitParentVTDno\"]  #NOTE: some units are VTD fragments, so they share a parentVTDno\n",
    "uVLstring = topDF[\"unitVTDlist\"]\n",
    "unitNbrs = [ast.literal_eval(uVLstring[u]) for u in range(nUnits)]\n",
    "allUnits = topDF[\"allUnits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5f3a5392-11f1-4061-a458-aecd834b2748",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working avg precinct-to-HDcP distance for HD 0\n",
      "working avg precinct-to-HDcP distance for HD 800\n",
      "working avg precinct-to-HDcP distance for HD 1600\n",
      "working avg precinct-to-HDcP distance for HD 2400\n",
      "working avg precinct-to-HDcP distance for HD 3200\n",
      "working avg precinct-to-HDcP distance for HD 4000\n",
      "working avg precinct-to-HDcP distance for HD 4800\n",
      "working avg precinct-to-HDcP distance for HD 5600\n",
      "all avgDist to HD centers computed\n"
     ]
    }
   ],
   "source": [
    "#needed for restart only  about 5sec per 2000 HDs\n",
    "avgDist = [0. for t in range(nHDs)]\n",
    "popHDlist = list()\n",
    "for t in range(nHDs):\n",
    "    if t%800 == 0:\n",
    "        print(\"working avg precinct-to-HDcP distance for HD\",t)\n",
    "    if HDvPop[t] > 0.1 * aDP:\n",
    "        avgDist[t] = np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "        popHDlist.append(t)\n",
    "print(\"all avgDist to HD centers computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ea0db02f-9df8-4906-acb4-086c29c0fcc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking for discontiguities for HD 0\n",
      "checking for discontiguities for HD 500\n",
      "checking for discontiguities for HD 1000\n",
      "checking for discontiguities for HD 2000\n",
      "checking for discontiguities for HD 2500\n",
      "checking for discontiguities for HD 4000\n",
      "checking for discontiguities for HD 4500\n",
      "checking for discontiguities for HD 5000\n",
      "checking for discontiguities for HD 5500\n",
      "checking for discontiguities for HD 9000\n",
      "checking for discontiguities for HD 9500\n",
      "checking for discontiguities for HD 10000\n",
      "checking for discontiguities for HD 14000\n",
      "out of 6033 total HDs, there were 6033 0 0 0 HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\n"
     ]
    }
   ],
   "source": [
    "discontigOnly, enclaveOnly, bothProblems, cleanList = list(), list(), list(), list()  #optional contiguity check\n",
    "for t in popHDlist:\n",
    "    if t%500 == 0:\n",
    "        print(\"checking for discontiguities for HD\",t)\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and noEnclave:\n",
    "        cleanList.append(t)\n",
    "    if unbroken and not noEnclave:\n",
    "        enclaveOnly.append(t)\n",
    "    if not unbroken and noEnclave:\n",
    "        discontigOnly.append(t)\n",
    "    if not unbroken and not noEnclave:\n",
    "        bothProblems.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",len(cleanList),len(discontigOnly),len(enclaveOnly),\n",
    "      len(bothProblems),\"HDs that were clean, discontig only, enclave-only, both problems.  Should all be zeroes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "27b42f93-dc87-4afe-8eb3-f30fdff4a737",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking unitUse, pop for county clusters\n",
      "5839 0.97225 127657.0\n",
      "5840 0.97639 164779.0\n"
     ]
    }
   ],
   "source": [
    "print(\"checking unitUse, pop for county clusters\")\n",
    "for uNo in allUnits:\n",
    "    if uNo - int(uNo) > 0.23 and uNo - int(uNo) < 0.27:\n",
    "        u = allUnits.index(uNo)\n",
    "        print(u,r5(unitUse[u]), unitPop[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "72941b8f-7be6-4a31-9377-618f44a34336",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unpatchedUnitUse = unitUse.copy()              #... for safekeeping\n",
    "unpatchedHDlist =  [HDunitList[t].copy() for t in range(nHDs) ]\n",
    "unpatchedHDpop =   HDvPop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5f9ca091-1eb6-4135-a233-7f5ae0f5e3d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "unitUse = unpatchedUnitUse.copy()              #... for restarting\n",
    "HDunitList =  [unpatchedHDlist[t].copy() for t in range(nHDs) ]\n",
    "HDvPop =   unpatchedHDpop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc95ca58-c27f-4ac9-b6f7-234a94945f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I believe BELOW UNDERUSED is faster now that we subbed wontEnclave for enclaveCheck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e9dd457d-fb37-49cf-9b19-19c17c328ede",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here, we exchange in under-used, THEN shed over-used\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.07\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 1998 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 300\n",
      "enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97 0.97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 2880 units out of 5841 with usage below 0.97\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n",
      "enter 1 to print out stats for every patched HD, otherwise enter reporting frequency 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's further tighten the distro with exchanges up to 38875\n",
      "current avg and SD of unit usage are 1.00068 0.13075 . Now trying to increase up to 300 units' underusage\n",
      "all done trying to increase usage of unit 2401 final usage = 0.9725 784 sec elapsed 170 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00066 0.12786\n",
      "all done trying to increase usage of unit 2567 final usage = 0.90995 2123 sec elapsed 135 2 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00066 0.12642\n",
      "all done trying to increase usage of unit 4955 final usage = 0.77164 2219 sec elapsed 33 2 successful, failed patches, couldn't start= 24\n",
      "current avg and SD of usage are 1.00066 0.12576\n",
      "all done trying to increase usage of unit 2554 final usage = 0.93551 3622 sec elapsed 129 2 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.00066 0.12457\n",
      "all done trying to increase usage of unit 4817 final usage = 0.79208 3758 sec elapsed 40 0 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.00065 0.12385\n",
      "all done trying to increase usage of unit 5062 final usage = 0.81654 3883 sec elapsed 42 0 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00065 0.12325\n",
      "all done trying to increase usage of unit 4830 final usage = 0.80468 4051 sec elapsed 33 1 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00065 0.12267\n",
      "all done trying to increase usage of unit 2564 final usage = 0.97047 5950 sec elapsed 145 6 successful, failed patches, couldn't start= 42\n",
      "current avg and SD of usage are 1.00067 0.12008\n",
      "all done trying to increase usage of unit 2565 final usage = 0.82705 6377 sec elapsed 53 1 successful, failed patches, couldn't start= 86\n",
      "current avg and SD of usage are 1.00066 0.11936\n",
      "begin usage increase for unit 5019 with usage 0.76169 . Sec, total UU units tried = 6378 10\n",
      "all done trying to increase usage of unit 5019 final usage = 0.81305 6615 sec elapsed 34 3 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00066 0.11893\n",
      "all done trying to increase usage of unit 2396 final usage = 0.95953 8283 sec elapsed 127 13 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00066 0.11799\n",
      "all done trying to increase usage of unit 2402 final usage = 0.94675 10121 sec elapsed 119 11 successful, failed patches, couldn't start= 80\n",
      "current avg and SD of usage are 1.00066 0.11688\n",
      "all done trying to increase usage of unit 5011 final usage = 0.82195 10332 sec elapsed 32 2 successful, failed patches, couldn't start= 35\n",
      "current avg and SD of usage are 1.00066 0.11642\n",
      "all done trying to increase usage of unit 3814 final usage = 0.97189 12715 sec elapsed 151 0 successful, failed patches, couldn't start= 50\n",
      "current avg and SD of usage are 1.00067 0.11402\n",
      "all done trying to increase usage of unit 4950 final usage = 0.86978 12767 sec elapsed 20 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00068 0.1133\n",
      "all done trying to increase usage of unit 5001 final usage = 0.84003 12868 sec elapsed 33 0 successful, failed patches, couldn't start= 25\n",
      "current avg and SD of usage are 1.00068 0.11273\n",
      "all done trying to increase usage of unit 429 final usage = 0.97021 14625 sec elapsed 114 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00067 0.11056\n",
      "all done trying to increase usage of unit 2568 final usage = 0.86443 15073 sec elapsed 45 1 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 1.00068 0.1101\n",
      "all done trying to increase usage of unit 4919 final usage = 0.90718 15227 sec elapsed 39 0 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 1.00067 0.10932\n",
      "begin usage increase for unit 3285 with usage 0.78968 . Sec, total UU units tried = 15229 20\n",
      "all done trying to increase usage of unit 3285 final usage = 0.97022 17991 sec elapsed 145 0 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00067 0.10741\n",
      "all done trying to increase usage of unit 4883 final usage = 0.85803 18262 sec elapsed 51 2 successful, failed patches, couldn't start= 40\n",
      "current avg and SD of usage are 1.00067 0.10678\n",
      "all done trying to increase usage of unit 520 final usage = 0.97653 21043 sec elapsed 117 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00067 0.10512\n",
      "all done trying to increase usage of unit 2973 final usage = 0.97047 22144 sec elapsed 113 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00067 0.10333\n",
      "all done trying to increase usage of unit 2581 final usage = 0.86578 22982 sec elapsed 47 1 successful, failed patches, couldn't start= 130\n",
      "current avg and SD of usage are 1.00067 0.10297\n",
      "all done trying to increase usage of unit 4155 final usage = 0.97068 24730 sec elapsed 122 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00067 0.10152\n",
      "all done trying to increase usage of unit 4894 final usage = 0.87413 24952 sec elapsed 44 2 successful, failed patches, couldn't start= 23\n",
      "current avg and SD of usage are 1.00067 0.10092\n",
      "all done trying to increase usage of unit 2888 final usage = 0.97146 28223 sec elapsed 125 0 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 1.00067 0.09936\n",
      "all done trying to increase usage of unit 3136 final usage = 0.97083 31697 sec elapsed 116 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00067 0.09793\n",
      "all done trying to increase usage of unit 4831 final usage = 0.91871 32164 sec elapsed 73 1 successful, failed patches, couldn't start= 11\n",
      "current avg and SD of usage are 1.00067 0.09714\n",
      "begin usage increase for unit 1573 with usage 0.80574 . Sec, total UU units tried = 32165 30\n",
      "all done trying to increase usage of unit 1573 final usage = 0.97276 32855 sec elapsed 98 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00066 0.09401\n",
      "all done trying to increase usage of unit 2370 final usage = 0.97119 34816 sec elapsed 110 3 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00067 0.09339\n",
      "all done trying to increase usage of unit 547 final usage = 0.9763 37406 sec elapsed 104 2 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00066 0.0922\n",
      "all done trying to increase usage of unit 2484 final usage = 0.89756 37881 sec elapsed 54 0 successful, failed patches, couldn't start= 110\n",
      "current avg and SD of usage are 1.00066 0.09135\n",
      "all done trying to increase usage of unit 4118 final usage = 0.97065 39534 sec elapsed 100 0 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00065 0.09014\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[127], line 99\u001b[0m\n\u001b[0;32m     97\u001b[0m listNo \u001b[38;5;241m=\u001b[39m idx[ij]   \u001b[38;5;66;03m#work from low to high score\u001b[39;00m\n\u001b[0;32m     98\u001b[0m addU \u001b[38;5;241m=\u001b[39m addCandidates[listNo]\n\u001b[1;32m---> 99\u001b[0m cContig \u001b[38;5;241m=\u001b[39m \u001b[43mwontEnclave\u001b[49m\u001b[43m(\u001b[49m\u001b[43maddU\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrySet\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munitNbrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mborderUnits\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m#4/20/24 sub this in as faster? vs below line\u001b[39;00m\n\u001b[0;32m    100\u001b[0m \u001b[38;5;66;03m#contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\u001b[39;00m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cContig: \u001b[38;5;66;03m#contig and cContig:\u001b[39;00m\n",
      "Cell \u001b[1;32mIn[2], line 809\u001b[0m, in \u001b[0;36mwontEnclave\u001b[1;34m(proposedU, dList, NBRLIST, mapBDRYLIST)\u001b[0m\n\u001b[0;32m    807\u001b[0m                 adjSet \u001b[38;5;241m=\u001b[39m adjSet\u001b[38;5;241m.\u001b[39munion( \u001b[38;5;28mset\u001b[39m(NBRLIST[UU])\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mset\u001b[39m(newList)) )\n\u001b[0;32m    808\u001b[0m             ADJLIST \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(adjSet)\n\u001b[1;32m--> 809\u001b[0m             wontEnclave, __ \u001b[38;5;241m=\u001b[39m   \u001b[43misContiguous\u001b[49m\u001b[43m(\u001b[49m\u001b[43mADJLIST\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mNBRLIST\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    811\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wontEnclave\n",
      "Cell \u001b[1;32mIn[2], line -1\u001b[0m, in \u001b[0;36misContiguous\u001b[1;34m(VLIST, NEIGHBORLIST, returnBiggestPiece)\u001b[0m\n\u001b[0;32m      0\u001b[0m <Error retrieving source code with stack_data see ipython/ipython#13598>\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#FIRST PATCHING BLOCK - boosting underuse.  Suppresses long strings.  For some states (e.g. MA), do overused first. MA:over-und-over-und\n",
    "print(\"Here, we exchange in under-used, THEN shed over-used\")\n",
    "maxSD = 0.07  #0.07  #0.05   #adjust down if distro already tight\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.97\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "nSmallUsers = 5\n",
    "maxExchangePop = 0.05*aDP \n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP\n",
    "debug1 = int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries: \n",
    "    #each round, find the (5) most underused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nSmallFound, smallUsers = 0,0,list()\n",
    "    while nSmallFound < nSmallUsers:\n",
    "        consideredSmallU = idx[idxNo]\n",
    "        if consideredSmallU not in attemptedSmallUs:\n",
    "            smallUsers.append(consideredSmallU)\n",
    "            nSmallFound +=1\n",
    "        idxNo +=1\n",
    "    UUUclusters = [ [b] + unitNbrs[b] for b in smallUsers ]\n",
    "    smallUnderUse = [np.sum([(unitUse[j] - 1.) for j in UUUclusters[i] ]) for i in range(nSmallUsers) ]\n",
    "    smallI = smallUnderUse.index(np.max(smallUnderUse))\n",
    "    UUU = smallUsers[ smallI ]  #pick the unit that centers cluster with least composite use\n",
    "    attemptedSmallUs.append(UUU)  #so we don't try this unit again in a future loop\n",
    "    UUUc = UUUclusters[smallI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUc) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    idx0 = np.argsort(uuuDists)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs): #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 20 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(set(UUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            if abs(excess) <= 2.*maxGap:  #giving ourselves a bit more margin after exchanges\n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying to boost usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"worked, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2377b41-61df-4f0d-9d87-a0557362328d",
   "metadata": {},
   "outputs": [],
   "source": [
    ".99959 0.09151  8:44 0.08996  #NYupper underuse patch stopped after 11 hours.  save and start overuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "861f29d3-b727-49af-90a8-aa9a963e0741",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00068 1.00065 and their SDs are 0.13075 0.08918\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 14191\n",
      "working on HD 300 out of 14191\n",
      "working on HD 600 out of 14191\n",
      "working on HD 900 out of 14191\n",
      "working on HD 1200 out of 14191\n",
      "working on HD 1800 out of 14191\n",
      "working on HD 2100 out of 14191\n",
      "working on HD 3900 out of 14191\n",
      "working on HD 4500 out of 14191\n",
      "working on HD 4800 out of 14191\n",
      "working on HD 5100 out of 14191\n",
      "working on HD 8700 out of 14191\n",
      "working on HD 9000 out of 14191\n",
      "working on HD 9300 out of 14191\n",
      "working on HD 9600 out of 14191\n",
      "working on HD 9900 out of 14191\n",
      "working on HD 14100 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1ee72cf0-618e-411f-aa37-518ac2c679ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "#optional intermediate save\n",
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"underPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "77c07848-50c2-4fe9-8841-768b02bf4ac4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.08\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 2031 units out of 5841 with usage above 1.03\n",
      "maxExchangePop is currently 0.05 fraction of avgDistrictPop\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter updated maxExchangePop fraction 0.05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this block reduces overuse, with exchanges up to 38875\n",
      "current avg and SD of unit usage are 1.00065 0.08918 . Now trying to reduce up to 2031 units' overusage\n",
      "starting to reduce usage for unit 20 with usage 1.32097 . Total units tried = 1\n",
      "try to drop unit 20 from 66 th HD.  usage down to 1.220482638964808\n",
      "try to drop unit 20 from 132 th HD.  usage down to 1.1326157498807128\n",
      "all done trying to reduce usage of unit 20 final usage = 1.02777 265 sec elapsed 185 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00061 0.08625\n",
      "starting to reduce usage for unit 2857 with usage 1.22727 . Total units tried = 2\n",
      "try to drop unit 2857 from 42 th HD.  usage down to 1.2191411891958361\n",
      "try to drop unit 2857 from 84 th HD.  usage down to 1.2099465349310423\n",
      "try to drop unit 2857 from 126 th HD.  usage down to 1.206381339571971\n",
      "try to drop unit 2857 from 168 th HD.  usage down to 1.196539754114675\n",
      "try to drop unit 2857 from 210 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2857 final usage = 1.18506 502 sec elapsed 31 10 successful, failed patches, couldn't start= 172\n",
      "current avg and SD of usage are 1.0006 0.08558\n",
      "starting to reduce usage for unit 1909 with usage 1.19835 . Total units tried = 3\n",
      "try to drop unit 1909 from 48 th HD.  usage down to 1.1606858756786267\n",
      "try to drop unit 1909 from 96 th HD.  usage down to 1.0996441235368755\n",
      "try to drop unit 1909 from 144 th HD.  usage down to 1.0423810669091713\n",
      "all done trying to reduce usage of unit 1909 final usage = 1.02812 1282 sec elapsed 112 5 successful, failed patches, couldn't start= 39\n",
      "current avg and SD of usage are 1.00058 0.08322\n",
      "starting to reduce usage for unit 641 with usage 1.19542 . Total units tried = 4\n",
      "try to drop unit 641 from 46 th HD.  usage down to 1.1647783906975717\n",
      "try to drop unit 641 from 92 th HD.  usage down to 1.1170984042792687\n",
      "try to drop unit 641 from 138 th HD.  usage down to 1.073208688684626\n",
      "all done trying to reduce usage of unit 641 final usage = 1.02976 2076 sec elapsed 112 0 successful, failed patches, couldn't start= 56\n",
      "current avg and SD of usage are 1.00055 0.08162\n",
      "starting to reduce usage for unit 2245 with usage 1.19231 . Total units tried = 5\n",
      "try to drop unit 2245 from 50 th HD.  usage down to 1.0950114274028775\n",
      "try to drop unit 2245 from 100 th HD.  usage down to 1.0314385408936393\n",
      "all done trying to reduce usage of unit 2245 final usage = 1.0291 2111 sec elapsed 87 0 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00054 0.08009\n",
      "starting to reduce usage for unit 4411 with usage 1.18537 . Total units tried = 6\n",
      "try to drop unit 4411 from 21 th HD.  usage down to 1.1651295084223001\n",
      "try to drop unit 4411 from 42 th HD.  usage down to 1.1411596145164424\n",
      "try to drop unit 4411 from 63 th HD.  usage down to 1.1147241796642122\n",
      "try to drop unit 4411 from 84 th HD.  usage down to 1.0833383707365656\n",
      "try to drop unit 4411 from 105 th HD.  usage down to 1.0472607029813827\n",
      "all done trying to reduce usage of unit 4411 final usage = 1.04316 2687 sec elapsed 97 5 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00053 0.07816\n",
      "starting to reduce usage for unit 2758 with usage 1.18506 . Total units tried = 7\n",
      "try to drop unit 2758 from 22 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2758 from 44 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2758 from 66 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2758 from 88 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2758 from 110 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2758 final usage = 1.18506 2695 sec elapsed 0 0 successful, failed patches, couldn't start= 110\n",
      "current avg and SD of usage are 1.00053 0.07816\n",
      "starting to reduce usage for unit 2354 with usage 1.15578 . Total units tried = 8\n",
      "try to drop unit 2354 from 56 th HD.  usage down to 1.1192912823770802\n",
      "try to drop unit 2354 from 112 th HD.  usage down to 1.1027643125487654\n",
      "try to drop unit 2354 from 168 th HD.  usage down to 1.0937111342902832\n",
      "try to drop unit 2354 from 224 th HD.  usage down to 1.070527075292214\n",
      "try to drop unit 2354 from 280 th HD.  usage down to 1.0436749292941176\n",
      "all done trying to reduce usage of unit 2354 final usage = 1.04119 3394 sec elapsed 90 0 successful, failed patches, couldn't start= 193\n",
      "current avg and SD of usage are 1.00051 0.07683\n",
      "starting to reduce usage for unit 1502 with usage 1.15468 . Total units tried = 9\n",
      "try to drop unit 1502 from 16 th HD.  usage down to 1.1264435375689434\n",
      "try to drop unit 1502 from 32 th HD.  usage down to 1.0978808180399835\n",
      "try to drop unit 1502 from 48 th HD.  usage down to 1.0776523214283338\n",
      "try to drop unit 1502 from 64 th HD.  usage down to 1.0527409690077458\n",
      "try to drop unit 1502 from 80 th HD.  usage down to 1.0310539833855683\n",
      "all done trying to reduce usage of unit 1502 final usage = 1.0299 3493 sec elapsed 71 2 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00049 0.07532\n",
      "starting to reduce usage for unit 1892 with usage 1.1534 . Total units tried = 10\n",
      "try to drop unit 1892 from 51 th HD.  usage down to 1.1121377410397832\n",
      "try to drop unit 1892 from 102 th HD.  usage down to 1.0476684111087307\n",
      "all done trying to reduce usage of unit 1892 final usage = 1.02899 3718 sec elapsed 81 23 successful, failed patches, couldn't start= 20\n",
      "current avg and SD of usage are 1.00047 0.07397\n",
      "starting to reduce usage for unit 4311 with usage 1.1478 . Total units tried = 11\n",
      "try to drop unit 4311 from 44 th HD.  usage down to 1.089801251934019\n",
      "try to drop unit 4311 from 88 th HD.  usage down to 1.049087029608334\n",
      "all done trying to reduce usage of unit 4311 final usage = 1.02841 3868 sec elapsed 70 20 successful, failed patches, couldn't start= 14\n",
      "current avg and SD of usage are 1.00047 0.07258\n",
      "starting to reduce usage for unit 4681 with usage 1.14473 . Total units tried = 12\n",
      "try to drop unit 4681 from 36 th HD.  usage down to 1.0777809359795303\n",
      "all done trying to reduce usage of unit 4681 final usage = 1.0287 4016 sec elapsed 58 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00045 0.07152\n",
      "starting to reduce usage for unit 4771 with usage 1.13632 . Total units tried = 13\n",
      "try to drop unit 4771 from 35 th HD.  usage down to 1.1173183351617897\n",
      "try to drop unit 4771 from 70 th HD.  usage down to 1.0858514990668873\n",
      "all done trying to reduce usage of unit 4771 final usage = 1.0298 4169 sec elapsed 68 31 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00043 0.07025\n",
      "starting to reduce usage for unit 3990 with usage 1.13418 . Total units tried = 14\n",
      "try to drop unit 3990 from 33 th HD.  usage down to 1.0892366340543091\n",
      "try to drop unit 3990 from 66 th HD.  usage down to 1.040111020080601\n",
      "all done trying to reduce usage of unit 3990 final usage = 1.02902 4278 sec elapsed 58 0 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00042 0.06916\n",
      "starting to reduce usage for unit 1292 with usage 1.12787 . Total units tried = 15\n",
      "try to drop unit 1292 from 33 th HD.  usage down to 1.101865296835992\n",
      "try to drop unit 1292 from 66 th HD.  usage down to 1.063646196803453\n",
      "all done trying to reduce usage of unit 1292 final usage = 1.02995 4447 sec elapsed 60 0 successful, failed patches, couldn't start= 26\n",
      "current avg and SD of usage are 1.00041 0.06828\n",
      "starting to reduce usage for unit 4468 with usage 1.12201 . Total units tried = 16\n",
      "try to drop unit 4468 from 45 th HD.  usage down to 1.0822387163238725\n",
      "try to drop unit 4468 from 90 th HD.  usage down to 1.036783761641212\n",
      "all done trying to reduce usage of unit 4468 final usage = 1.02966 4802 sec elapsed 63 15 successful, failed patches, couldn't start= 16\n",
      "current avg and SD of usage are 1.00041 0.06716\n",
      "starting to reduce usage for unit 5400 with usage 1.11477 . Total units tried = 17\n",
      "try to drop unit 5400 from 39 th HD.  usage down to 1.0608771255162532\n",
      "try to drop unit 5400 from 78 th HD.  usage down to 1.033887361948385\n",
      "all done trying to reduce usage of unit 5400 final usage = 1.02863 4904 sec elapsed 51 13 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00041 0.06642\n",
      "starting to reduce usage for unit 5081 with usage 1.11422 . Total units tried = 18\n",
      "try to drop unit 5081 from 21 th HD.  usage down to 1.0769963872172512\n",
      "try to drop unit 5081 from 42 th HD.  usage down to 1.056884929847187\n",
      "try to drop unit 5081 from 63 th HD.  usage down to 1.0400029838575815\n",
      "all done trying to reduce usage of unit 5081 final usage = 1.02941 4992 sec elapsed 51 2 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.0004 0.06539\n",
      "starting to reduce usage for unit 2757 with usage 1.18506 . Total units tried = 19\n",
      "try to drop unit 2757 from 24 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2757 from 48 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2757 from 72 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2757 from 96 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2757 from 120 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2757 final usage = 1.18506 5000 sec elapsed 0 0 successful, failed patches, couldn't start= 121\n",
      "current avg and SD of usage are 1.0004 0.06539\n",
      "starting to reduce usage for unit 4320 with usage 1.11075 . Total units tried = 20\n",
      "try to drop unit 4320 from 32 th HD.  usage down to 1.0936712637794255\n",
      "try to drop unit 4320 from 64 th HD.  usage down to 1.071327057800652\n",
      "try to drop unit 4320 from 96 th HD.  usage down to 1.0526200713296143\n",
      "all done trying to reduce usage of unit 4320 final usage = 1.02627 5227 sec elapsed 50 29 successful, failed patches, couldn't start= 33\n",
      "current avg and SD of usage are 1.00039 0.06429\n",
      "starting to reduce usage for unit 1044 with usage 1.10811 . Total units tried = 21\n",
      "try to drop unit 1044 from 39 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1044 from 78 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1044 from 117 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1044 from 156 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1044 from 195 th HD.  usage down to 1.1081069610053558\n",
      "all done trying to reduce usage of unit 1044 final usage = 1.10811 5254 sec elapsed 0 0 successful, failed patches, couldn't start= 195\n",
      "current avg and SD of usage are 1.00039 0.06429\n",
      "starting to reduce usage for unit 2745 with usage 1.18506 . Total units tried = 22\n",
      "try to drop unit 2745 from 24 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2745 from 48 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2745 from 72 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2745 from 96 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2745 from 120 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2745 final usage = 1.18506 5262 sec elapsed 0 0 successful, failed patches, couldn't start= 121\n",
      "current avg and SD of usage are 1.00039 0.06429\n",
      "starting to reduce usage for unit 1055 with usage 1.10811 . Total units tried = 23\n",
      "try to drop unit 1055 from 24 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1055 from 48 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1055 from 72 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1055 from 96 th HD.  usage down to 1.1081069610053558\n",
      "try to drop unit 1055 from 120 th HD.  usage down to 1.1081069610053558\n",
      "all done trying to reduce usage of unit 1055 final usage = 1.10811 5269 sec elapsed 0 0 successful, failed patches, couldn't start= 121\n",
      "current avg and SD of usage are 1.00039 0.06429\n",
      "starting to reduce usage for unit 1057 with usage 1.10811 . Total units tried = 24\n",
      "try to drop unit 1057 from 24 th HD.  usage down to 1.0733707430191242\n",
      "try to drop unit 1057 from 48 th HD.  usage down to 1.040090441752401\n",
      "all done trying to reduce usage of unit 1057 final usage = 1.02699 5384 sec elapsed 54 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00038 0.06356\n",
      "starting to reduce usage for unit 5834 with usage 1.10435 . Total units tried = 25\n",
      "try to drop unit 5834 from 43 th HD.  usage down to 1.0749642773083778\n",
      "try to drop unit 5834 from 86 th HD.  usage down to 1.0579459998945062\n",
      "all done trying to reduce usage of unit 5834 final usage = 1.02966 5798 sec elapsed 49 13 successful, failed patches, couldn't start= 62\n",
      "current avg and SD of usage are 1.00038 0.06267\n",
      "starting to reduce usage for unit 1957 with usage 1.09787 . Total units tried = 26\n",
      "try to drop unit 1957 from 32 th HD.  usage down to 1.0575254303121497\n",
      "all done trying to reduce usage of unit 1957 final usage = 1.02977 6541 sec elapsed 53 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00038 0.06189\n",
      "starting to reduce usage for unit 711 with usage 1.09596 . Total units tried = 27\n",
      "try to drop unit 711 from 42 th HD.  usage down to 1.0548798289940982\n",
      "all done trying to reduce usage of unit 711 final usage = 1.02988 6691 sec elapsed 42 0 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 1.00038 0.06128\n",
      "starting to reduce usage for unit 2199 with usage 1.0958 . Total units tried = 28\n",
      "try to drop unit 2199 from 23 th HD.  usage down to 1.0564412096455775\n",
      "all done trying to reduce usage of unit 2199 final usage = 1.02729 6729 sec elapsed 33 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00037 0.06062\n",
      "starting to reduce usage for unit 3116 with usage 1.09587 . Total units tried = 29\n",
      "try to drop unit 3116 from 43 th HD.  usage down to 1.0352082333891492\n",
      "all done trying to reduce usage of unit 3116 final usage = 1.02997 6822 sec elapsed 39 1 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00037 0.06\n",
      "starting to reduce usage for unit 5808 with usage 1.09432 . Total units tried = 30\n",
      "try to drop unit 5808 from 22 th HD.  usage down to 1.0576630478819524\n",
      "all done trying to reduce usage of unit 5808 final usage = 1.02863 6861 sec elapsed 37 0 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00037 0.05947\n",
      "starting to reduce usage for unit 1484 with usage 1.09053 . Total units tried = 31\n",
      "try to drop unit 1484 from 29 th HD.  usage down to 1.0664589970379936\n",
      "try to drop unit 1484 from 58 th HD.  usage down to 1.0414614728681004\n",
      "all done trying to reduce usage of unit 1484 final usage = 1.02949 6932 sec elapsed 41 6 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00036 0.05903\n",
      "starting to reduce usage for unit 3514 with usage 1.09147 . Total units tried = 32\n",
      "try to drop unit 3514 from 38 th HD.  usage down to 1.033452644765327\n",
      "all done trying to reduce usage of unit 3514 final usage = 1.0284 6975 sec elapsed 33 1 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00036 0.05835\n",
      "starting to reduce usage for unit 4313 with usage 1.08686 . Total units tried = 33\n",
      "try to drop unit 4313 from 35 th HD.  usage down to 1.0762118384549544\n",
      "try to drop unit 4313 from 70 th HD.  usage down to 1.0572669150642142\n",
      "try to drop unit 4313 from 105 th HD.  usage down to 1.036244866671703\n",
      "all done trying to reduce usage of unit 4313 final usage = 1.02965 7083 sec elapsed 42 31 successful, failed patches, couldn't start= 37\n",
      "current avg and SD of usage are 1.00036 0.05772\n",
      "starting to reduce usage for unit 2759 with usage 1.18506 . Total units tried = 34\n",
      "try to drop unit 2759 from 25 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2759 from 50 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2759 from 75 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2759 from 100 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2759 from 125 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2759 final usage = 1.18506 7091 sec elapsed 0 0 successful, failed patches, couldn't start= 127\n",
      "current avg and SD of usage are 1.00036 0.05772\n",
      "starting to reduce usage for unit 2752 with usage 1.18506 . Total units tried = 35\n",
      "try to drop unit 2752 from 25 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2752 from 50 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2752 from 75 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2752 from 100 th HD.  usage down to 1.1850647638572265\n",
      "try to drop unit 2752 from 125 th HD.  usage down to 1.1850647638572265\n",
      "all done trying to reduce usage of unit 2752 final usage = 1.18506 7099 sec elapsed 0 0 successful, failed patches, couldn't start= 127\n",
      "current avg and SD of usage are 1.00036 0.05772\n",
      "starting to reduce usage for unit 2750 with usage 1.08396 . Total units tried = 36\n",
      "try to drop unit 2750 from 46 th HD.  usage down to 1.0601363057013282\n",
      "try to drop unit 2750 from 92 th HD.  usage down to 1.0472607029813852\n",
      "try to drop unit 2750 from 138 th HD.  usage down to 1.0348301066085703\n",
      "all done trying to reduce usage of unit 2750 final usage = 1.02904 7545 sec elapsed 50 22 successful, failed patches, couldn't start= 89\n",
      "current avg and SD of usage are 1.00035 0.0572\n",
      "starting to reduce usage for unit 4252 with usage 1.0863 . Total units tried = 37\n",
      "try to drop unit 4252 from 33 th HD.  usage down to 1.042679452667898\n",
      "all done trying to reduce usage of unit 4252 final usage = 1.0293 8992 sec elapsed 32 1 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00034 0.0565\n",
      "starting to reduce usage for unit 2022 with usage 1.081 . Total units tried = 38\n",
      "try to drop unit 2022 from 33 th HD.  usage down to 1.0490252946237881\n",
      "all done trying to reduce usage of unit 2022 final usage = 1.02912 9654 sec elapsed 41 3 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00034 0.0559\n",
      "starting to reduce usage for unit 2257 with usage 1.07994 . Total units tried = 39\n",
      "try to drop unit 2257 from 25 th HD.  usage down to 1.0490021440045665\n",
      "all done trying to reduce usage of unit 2257 final usage = 1.02969 9750 sec elapsed 31 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00033 0.05522\n",
      "starting to reduce usage for unit 1416 with usage 1.08048 . Total units tried = 40\n",
      "try to drop unit 1416 from 45 th HD.  usage down to 1.039161844692797\n",
      "all done trying to reduce usage of unit 1416 final usage = 1.02833 9868 sec elapsed 29 16 successful, failed patches, couldn't start= 13\n",
      "current avg and SD of usage are 1.00033 0.05463\n",
      "starting to reduce usage for unit 805 with usage 1.07769 . Total units tried = 41\n",
      "try to drop unit 805 from 28 th HD.  usage down to 1.0388608866430296\n",
      "all done trying to reduce usage of unit 805 final usage = 1.02954 10145 sec elapsed 33 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00033 0.05422\n",
      "starting to reduce usage for unit 2748 with usage 1.07862 . Total units tried = 42\n",
      "try to drop unit 2748 from 32 th HD.  usage down to 1.0641002061691032\n",
      "try to drop unit 2748 from 64 th HD.  usage down to 1.0391682754203209\n",
      "all done trying to reduce usage of unit 2748 final usage = 1.02972 10351 sec elapsed 37 10 successful, failed patches, couldn't start= 29\n",
      "current avg and SD of usage are 1.00033 0.05372\n",
      "starting to reduce usage for unit 5289 with usage 1.07561 . Total units tried = 43\n",
      "try to drop unit 5289 from 41 th HD.  usage down to 1.0428517961664934\n",
      "all done trying to reduce usage of unit 5289 final usage = 1.02813 10573 sec elapsed 26 22 successful, failed patches, couldn't start= 22\n",
      "current avg and SD of usage are 1.00032 0.0533\n",
      "starting to reduce usage for unit 4752 with usage 1.07582 . Total units tried = 44\n",
      "try to drop unit 4752 from 25 th HD.  usage down to 1.0515139861893628\n",
      "all done trying to reduce usage of unit 4752 final usage = 1.02921 10990 sec elapsed 30 10 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00032 0.05274\n",
      "starting to reduce usage for unit 1288 with usage 1.07221 . Total units tried = 45\n",
      "all done trying to reduce usage of unit 1288 final usage = 1.02965 11087 sec elapsed 28 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00032 0.05238\n",
      "starting to reduce usage for unit 3139 with usage 1.0717 . Total units tried = 46\n",
      "all done trying to reduce usage of unit 3139 final usage = 1.02904 11116 sec elapsed 20 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00031 0.05185\n",
      "starting to reduce usage for unit 3446 with usage 1.06982 . Total units tried = 47\n",
      "all done trying to reduce usage of unit 3446 final usage = 1.028 11154 sec elapsed 23 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.0003 0.05149\n",
      "starting to reduce usage for unit 373 with usage 1.06899 . Total units tried = 48\n",
      "try to drop unit 373 from 16 th HD.  usage down to 1.0393354743369312\n",
      "all done trying to reduce usage of unit 373 final usage = 1.02872 11235 sec elapsed 21 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.0003 0.05114\n",
      "starting to reduce usage for unit 5302 with usage 1.06893 . Total units tried = 49\n",
      "try to drop unit 5302 from 32 th HD.  usage down to 1.0386679648161798\n",
      "all done trying to reduce usage of unit 5302 final usage = 1.02917 11577 sec elapsed 27 1 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.0003 0.05072\n",
      "starting to reduce usage for unit 4300 with usage 1.07121 . Total units tried = 50\n",
      "try to drop unit 4300 from 30 th HD.  usage down to 1.0381959494133015\n",
      "all done trying to reduce usage of unit 4300 final usage = 1.02946 12011 sec elapsed 27 0 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00029 0.05031\n",
      "starting to reduce usage for unit 1468 with usage 1.06852 . Total units tried = 51\n",
      "try to drop unit 1468 from 16 th HD.  usage down to 1.0567074417665425\n",
      "try to drop unit 1468 from 32 th HD.  usage down to 1.043357251352699\n",
      "all done trying to reduce usage of unit 1468 final usage = 1.02918 12082 sec elapsed 25 1 successful, failed patches, couldn't start= 21\n",
      "current avg and SD of usage are 1.00029 0.04987\n",
      "starting to reduce usage for unit 2334 with usage 1.0687 . Total units tried = 52\n",
      "try to drop unit 2334 from 41 th HD.  usage down to 1.0524322940849078\n",
      "try to drop unit 2334 from 82 th HD.  usage down to 1.0371464546756108\n",
      "all done trying to reduce usage of unit 2334 final usage = 1.02853 12258 sec elapsed 24 43 successful, failed patches, couldn't start= 19\n",
      "current avg and SD of usage are 1.00029 0.04938\n",
      "starting to reduce usage for unit 2191 with usage 1.06579 . Total units tried = 53\n",
      "try to drop unit 2191 from 32 th HD.  usage down to 1.0303440310629932\n",
      "all done trying to reduce usage of unit 2191 final usage = 1.02765 12295 sec elapsed 20 7 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00028 0.04904\n",
      "starting to reduce usage for unit 1968 with usage 1.06596 . Total units tried = 54\n",
      "all done trying to reduce usage of unit 1968 final usage = 1.02934 12319 sec elapsed 18 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00028 0.04872\n",
      "starting to reduce usage for unit 4108 with usage 1.06318 . Total units tried = 55\n",
      "all done trying to reduce usage of unit 4108 final usage = 1.02928 13177 sec elapsed 23 1 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00028 0.04829\n",
      "starting to reduce usage for unit 3581 with usage 1.06257 . Total units tried = 56\n",
      "try to drop unit 3581 from 45 th HD.  usage down to 1.0540309729562407\n",
      "try to drop unit 3581 from 90 th HD.  usage down to 1.046563612113968\n",
      "try to drop unit 3581 from 135 th HD.  usage down to 1.044910915131136\n",
      "try to drop unit 3581 from 180 th HD.  usage down to 1.0392737393523426\n",
      "try to drop unit 3581 from 225 th HD.  usage down to 1.0316481826121022\n",
      "all done trying to reduce usage of unit 3581 final usage = 1.02922 13398 sec elapsed 23 4 successful, failed patches, couldn't start= 200\n",
      "current avg and SD of usage are 1.00027 0.04803\n",
      "starting to reduce usage for unit 4349 with usage 1.06073 . Total units tried = 57\n",
      "try to drop unit 4349 from 19 th HD.  usage down to 1.0607279326368284\n",
      "try to drop unit 4349 from 38 th HD.  usage down to 1.0497172409091862\n",
      "try to drop unit 4349 from 57 th HD.  usage down to 1.0312661973950243\n",
      "all done trying to reduce usage of unit 4349 final usage = 1.02964 13565 sec elapsed 21 8 successful, failed patches, couldn't start= 28\n",
      "current avg and SD of usage are 1.00027 0.04767\n",
      "starting to reduce usage for unit 2243 with usage 1.06004 . Total units tried = 58\n",
      "try to drop unit 2243 from 31 th HD.  usage down to 1.035624944534963\n",
      "all done trying to reduce usage of unit 2243 final usage = 1.02915 13642 sec elapsed 18 1 successful, failed patches, couldn't start= 17\n",
      "current avg and SD of usage are 1.00027 0.04731\n",
      "starting to reduce usage for unit 4778 with usage 1.06121 . Total units tried = 59\n",
      "try to drop unit 4778 from 33 th HD.  usage down to 1.0425984255006455\n",
      "all done trying to reduce usage of unit 4778 final usage = 1.02992 13899 sec elapsed 26 15 successful, failed patches, couldn't start= 4\n",
      "current avg and SD of usage are 1.00027 0.04694\n",
      "starting to reduce usage for unit 3517 with usage 1.05792 . Total units tried = 60\n",
      "all done trying to reduce usage of unit 3517 final usage = 1.02802 14009 sec elapsed 22 1 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00026 0.04667\n",
      "starting to reduce usage for unit 1170 with usage 1.05951 . Total units tried = 61\n",
      "try to drop unit 1170 from 19 th HD.  usage down to 1.0498329940052582\n",
      "try to drop unit 1170 from 38 th HD.  usage down to 1.0468928653649845\n",
      "try to drop unit 1170 from 57 th HD.  usage down to 1.0442266857187512\n",
      "try to drop unit 1170 from 76 th HD.  usage down to 1.0392583056061624\n",
      "all done trying to reduce usage of unit 1170 final usage = 1.02884 14096 sec elapsed 21 4 successful, failed patches, couldn't start= 61\n",
      "current avg and SD of usage are 1.00026 0.04634\n",
      "starting to reduce usage for unit 560 with usage 1.05706 . Total units tried = 62\n",
      "all done trying to reduce usage of unit 560 final usage = 1.02904 14141 sec elapsed 17 1 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00026 0.04617\n",
      "starting to reduce usage for unit 1417 with usage 1.05703 . Total units tried = 63\n",
      "try to drop unit 1417 from 18 th HD.  usage down to 1.0514908355701607\n",
      "try to drop unit 1417 from 36 th HD.  usage down to 1.0374679910535776\n",
      "all done trying to reduce usage of unit 1417 final usage = 1.02776 14208 sec elapsed 17 16 successful, failed patches, couldn't start= 15\n",
      "current avg and SD of usage are 1.00025 0.04592\n",
      "starting to reduce usage for unit 4750 with usage 1.0563 . Total units tried = 64\n",
      "all done trying to reduce usage of unit 4750 final usage = 1.02885 14606 sec elapsed 16 8 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00025 0.04557\n",
      "starting to reduce usage for unit 308 with usage 1.05694 . Total units tried = 65\n",
      "try to drop unit 308 from 21 th HD.  usage down to 1.0329047467772776\n",
      "all done trying to reduce usage of unit 308 final usage = 1.02551 14734 sec elapsed 17 0 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 1.00025 0.04531\n",
      "starting to reduce usage for unit 2270 with usage 1.05582 . Total units tried = 66\n",
      "try to drop unit 2270 from 22 th HD.  usage down to 1.055146061115048\n",
      "try to drop unit 2270 from 44 th HD.  usage down to 1.0378332563789476\n",
      "all done trying to reduce usage of unit 2270 final usage = 1.02489 14807 sec elapsed 16 36 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00025 0.04495\n",
      "starting to reduce usage for unit 4228 with usage 1.05549 . Total units tried = 67\n",
      "all done trying to reduce usage of unit 4228 final usage = 1.02984 15533 sec elapsed 12 0 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00025 0.04469\n",
      "starting to reduce usage for unit 4558 with usage 1.05528 . Total units tried = 68\n",
      "all done trying to reduce usage of unit 4558 final usage = 1.02998 15565 sec elapsed 14 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00025 0.0445\n",
      "starting to reduce usage for unit 4644 with usage 1.05515 . Total units tried = 69\n",
      "try to drop unit 4644 from 17 th HD.  usage down to 1.0514934078611564\n",
      "try to drop unit 4644 from 34 th HD.  usage down to 1.0410473340132553\n",
      "all done trying to reduce usage of unit 4644 final usage = 1.02916 15644 sec elapsed 15 22 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00024 0.04428\n",
      "starting to reduce usage for unit 2008 with usage 1.05503 . Total units tried = 70\n",
      "try to drop unit 2008 from 28 th HD.  usage down to 1.0360133604796073\n",
      "all done trying to reduce usage of unit 2008 final usage = 1.02927 15711 sec elapsed 15 16 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00024 0.04399\n",
      "starting to reduce usage for unit 115 with usage 1.05433 . Total units tried = 71\n",
      "all done trying to reduce usage of unit 115 final usage = 1.02846 15734 sec elapsed 21 0 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00024 0.04379\n",
      "starting to reduce usage for unit 5724 with usage 1.05374 . Total units tried = 72\n",
      "all done trying to reduce usage of unit 5724 final usage = 1.02901 15793 sec elapsed 18 0 successful, failed patches, couldn't start= 8\n",
      "current avg and SD of usage are 1.00024 0.0436\n",
      "starting to reduce usage for unit 1465 with usage 1.05252 . Total units tried = 73\n",
      "all done trying to reduce usage of unit 1465 final usage = 1.02915 15862 sec elapsed 14 7 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00024 0.04336\n",
      "starting to reduce usage for unit 4294 with usage 1.05087 . Total units tried = 74\n",
      "all done trying to reduce usage of unit 4294 final usage = 1.02982 15877 sec elapsed 13 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00024 0.04312\n",
      "starting to reduce usage for unit 190 with usage 1.05046 . Total units tried = 75\n",
      "all done trying to reduce usage of unit 190 final usage = 1.02607 15923 sec elapsed 13 0 successful, failed patches, couldn't start= 7\n",
      "current avg and SD of usage are 1.00023 0.04291\n",
      "starting to reduce usage for unit 2329 with usage 1.05245 . Total units tried = 76\n",
      "try to drop unit 2329 from 31 th HD.  usage down to 1.0442948514308799\n",
      "try to drop unit 2329 from 62 th HD.  usage down to 1.0304404919763597\n",
      "all done trying to reduce usage of unit 2329 final usage = 1.02927 16116 sec elapsed 15 43 successful, failed patches, couldn't start= 9\n",
      "current avg and SD of usage are 1.00023 0.04271\n",
      "starting to reduce usage for unit 3559 with usage 1.0482 . Total units tried = 77\n",
      "try to drop unit 3559 from 33 th HD.  usage down to 1.0407450898179569\n",
      "all done trying to reduce usage of unit 3559 final usage = 1.02808 16212 sec elapsed 11 18 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00023 0.04253\n",
      "starting to reduce usage for unit 4312 with usage 1.04739 . Total units tried = 78\n",
      "try to drop unit 4312 from 36 th HD.  usage down to 1.0391721338568591\n",
      "all done trying to reduce usage of unit 4312 final usage = 1.02915 16393 sec elapsed 15 13 successful, failed patches, couldn't start= 27\n",
      "current avg and SD of usage are 1.00023 0.04237\n",
      "starting to reduce usage for unit 2440 with usage 1.0468 . Total units tried = 79\n",
      "all done trying to reduce usage of unit 2440 final usage = 1.02833 16446 sec elapsed 12 0 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00023 0.0422\n",
      "starting to reduce usage for unit 4626 with usage 1.04621 . Total units tried = 80\n",
      "try to drop unit 4626 from 23 th HD.  usage down to 1.0434048387366273\n",
      "all done trying to reduce usage of unit 4626 final usage = 1.02894 16537 sec elapsed 7 22 successful, failed patches, couldn't start= 10\n",
      "current avg and SD of usage are 1.00023 0.04204\n",
      "starting to reduce usage for unit 3439 with usage 1.04594 . Total units tried = 81\n",
      "all done trying to reduce usage of unit 3439 final usage = 1.02965 16561 sec elapsed 7 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00023 0.04183\n",
      "starting to reduce usage for unit 5544 with usage 1.04577 . Total units tried = 82\n",
      "all done trying to reduce usage of unit 5544 final usage = 1.0244 16587 sec elapsed 11 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00022 0.04166\n",
      "starting to reduce usage for unit 4033 with usage 1.04575 . Total units tried = 83\n",
      "all done trying to reduce usage of unit 4033 final usage = 1.02795 16628 sec elapsed 13 1 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00022 0.04145\n",
      "starting to reduce usage for unit 5037 with usage 1.05043 . Total units tried = 84\n",
      "all done trying to reduce usage of unit 5037 final usage = 1.02943 16655 sec elapsed 10 1 successful, failed patches, couldn't start= 2\n",
      "current avg and SD of usage are 1.00022 0.04126\n",
      "starting to reduce usage for unit 2753 with usage 1.04409 . Total units tried = 85\n",
      "try to drop unit 2753 from 19 th HD.  usage down to 1.044089068148967\n",
      "try to drop unit 2753 from 38 th HD.  usage down to 1.044089068148967\n",
      "try to drop unit 2753 from 57 th HD.  usage down to 1.0399283874178695\n",
      "try to drop unit 2753 from 76 th HD.  usage down to 1.0389149047544683\n",
      "try to drop unit 2753 from 95 th HD.  usage down to 1.0313973842372326\n",
      "all done trying to reduce usage of unit 2753 final usage = 1.0314 16772 sec elapsed 9 1 successful, failed patches, couldn't start= 89\n",
      "current avg and SD of usage are 1.00022 0.0411\n",
      "starting to reduce usage for unit 4422 with usage 1.04387 . Total units tried = 86\n",
      "all done trying to reduce usage of unit 4422 final usage = 1.02861 16805 sec elapsed 8 0 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00022 0.04099\n",
      "starting to reduce usage for unit 4246 with usage 1.04373 . Total units tried = 87\n",
      "all done trying to reduce usage of unit 4246 final usage = 1.02894 17030 sec elapsed 10 0 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00022 0.04085\n",
      "starting to reduce usage for unit 1960 with usage 1.04375 . Total units tried = 88\n",
      "try to drop unit 1960 from 22 th HD.  usage down to 1.033686723248507\n",
      "all done trying to reduce usage of unit 1960 final usage = 1.02983 17163 sec elapsed 14 11 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00021 0.04073\n",
      "starting to reduce usage for unit 3883 with usage 1.04354 . Total units tried = 89\n",
      "all done trying to reduce usage of unit 3883 final usage = 1.02936 17173 sec elapsed 9 0 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00021 0.04056\n",
      "starting to reduce usage for unit 3986 with usage 1.04323 . Total units tried = 90\n",
      "all done trying to reduce usage of unit 3986 final usage = 1.02937 17178 sec elapsed 2 3 successful, failed patches, couldn't start= 0\n",
      "current avg and SD of usage are 1.00021 0.04049\n",
      "starting to reduce usage for unit 1483 with usage 1.04266 . Total units tried = 91\n",
      "try to drop unit 1483 from 28 th HD.  usage down to 1.0380300366422779\n",
      "all done trying to reduce usage of unit 1483 final usage = 1.02916 17257 sec elapsed 9 29 successful, failed patches, couldn't start= 12\n",
      "current avg and SD of usage are 1.00021 0.04039\n",
      "starting to reduce usage for unit 4607 with usage 1.04261 . Total units tried = 92\n",
      "try to drop unit 4607 from 41 th HD.  usage down to 1.0336095545177626\n",
      "all done trying to reduce usage of unit 4607 final usage = 1.02852 17488 sec elapsed 8 35 successful, failed patches, couldn't start= 6\n",
      "current avg and SD of usage are 1.00021 0.04026\n",
      "starting to reduce usage for unit 4768 with usage 1.04248 . Total units tried = 93\n",
      "all done trying to reduce usage of unit 4768 final usage = 1.02777 17545 sec elapsed 4 10 successful, failed patches, couldn't start= 3\n",
      "current avg and SD of usage are 1.00021 0.0401\n",
      "starting to reduce usage for unit 1228 with usage 1.04239 . Total units tried = 94\n",
      "all done trying to reduce usage of unit 1228 final usage = 1.02969 17593 sec elapsed 11 0 successful, failed patches, couldn't start= 1\n",
      "current avg and SD of usage are 1.00021 0.04003\n",
      "starting to reduce usage for unit 925 with usage 1.04222 . Total units tried = 95\n",
      "try to drop unit 925 from 23 th HD.  usage down to 1.0336327051369794\n",
      "try to drop unit 925 from 46 th HD.  usage down to 1.0304906516513328\n",
      "try to drop unit 925 from 69 th HD.  usage down to 1.0304906516513328\n",
      "all done trying to reduce usage of unit 925 final usage = 1.02943 17623 sec elapsed 8 1 successful, failed patches, couldn't start= 78\n",
      "current avg and SD of usage are 1.00021 0.03992\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  run second for most states except MA\n",
    "maxSD = 0.04 #0.06  #0.08\n",
    "stopMaxUse = 1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.05*aDP\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "newMEPratio = float(input(\"Enter updated maxExchangePop fraction\"))\n",
    "maxExchangePop = newMEPratio*aDP \n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #1/25/24\n",
    "maxGap = 0.9 * np.median(unitPop) \n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "#maxNtries = int(currSD * nUnits / nDistricts)   #try up to about 10% of the districts a unit is drawn into\n",
    "#maxNtries = 10 #overrirde\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #each round, find the (5) most overused not-yet-tried units. Pick the unit w/ most overuse of it + 1-nbrs\n",
    "    idx = np.argsort(unitUse)\n",
    "    idxNo, nBigFound, bigUsers = 0,0,list()\n",
    "    while nBigFound < nBigUsers:\n",
    "        consideredBigU = idx[-idxNo-1]\n",
    "        if consideredBigU not in attemptedBigUs:\n",
    "            bigUsers.append(consideredBigU)\n",
    "            nBigFound +=1\n",
    "        idxNo +=1\n",
    "    OUUclusters = [ [b] + unitNbrs[b] for b in bigUsers ]\n",
    "    bigOverUse = [np.sum([(unitUse[j] - 1.) for j in OUUclusters[i] ]) for i in range(nBigUsers) ]\n",
    "    bigI = bigOverUse.index(np.max(bigOverUse))\n",
    "    OUU = bigUsers[ bigI ]  #pick the unit that centers cluster with most overuse\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    OUUc = OUUclusters[bigI]  #the list of this unit and ALL its neighbors (to be curated below ...)\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUU2nbrSet = set( unitNbrs[OUU])\n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.union(set(unitNbrs[u])) \n",
    "    for u in OUUc:\n",
    "        OUU2nbrSet = OUU2nbrSet.difference({u})\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUU2nbrSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.5*len(ouuHDs): #arbitrarily only examine 50% of HDs, biasing farthest ones\n",
    "        idxNo -=1\n",
    "        if idxNo % int(0.1*len(ouuHDs)) == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD.  usage down to\",unitUse[OUU] )\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).difference(set(OUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(OUUc))\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative)  #NEW FOR GA 3/11/24\n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= 2.* maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f90621-e7fc-4891-a5cd-69033c3c48e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy one of two above blocks and run again if needed (n/a for NC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "cb60da52-ca13-45dd-8bf0-475213183a0d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underused units\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"overused units\")\n",
    "maxPlot = 1.06\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > maxPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()\n",
    "print(\"underused units\")\n",
    "minPlot = 0.94\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minPlot:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(round(unitUse[u],2),unitGeom[u])\n",
    "plotPoly(MAP,0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa912158-e8e9-46f3-8c3d-f6b17c26e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note - for MA, above is second run-through after overuse, then underuse pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0d4f1372-6044-4803-91d9-de4b95a40083",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unpatched, patched use avgs are 1.00068 1.00021 and their SDs are 0.13075 0.03992\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 14191\n",
      "working on HD 300 out of 14191\n",
      "working on HD 600 out of 14191\n",
      "working on HD 900 out of 14191\n",
      "working on HD 1200 out of 14191\n",
      "working on HD 1800 out of 14191\n",
      "working on HD 2100 out of 14191\n",
      "working on HD 3900 out of 14191\n",
      "working on HD 4500 out of 14191\n",
      "working on HD 4800 out of 14191\n",
      "working on HD 5100 out of 14191\n",
      "working on HD 8700 out of 14191\n",
      "working on HD 9000 out of 14191\n",
      "working on HD 9300 out of 14191\n",
      "working on HD 9600 out of 14191\n",
      "working on HD 9900 out of 14191\n",
      "working on HD 14100 out of 14191\n",
      "all done checking HD and complement contiguity for all 14191 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    for u in unpatchedHDlist[t]:\n",
    "        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%300 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ac9cd133-94f0-494b-ac74-0c584482d97e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets write these UNIT lists to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets write these UNIT lists to a file\")\n",
    "HDvPop = [0. for t in range(nHDs)]  #writing UNIT lists to a file\n",
    "tList = [t for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"fullyPatched.csv\" #\"contigUnpatchedB.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "26b87c95-101f-4f8f-bb73-feb1827ac6b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    }
   ],
   "source": [
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))\n",
    "if noFrag == 1:\n",
    "    nFragmentedVTDs,fragmentedVTDs = 0, list()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ec102553-e4f3-4010-86bc-725a01b95788",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([v for v in range(len(parentVTDno))],parentVTDno)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "55d64c64-b473-49d0-b863-d4123cbb9b6c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in range(nVTDs):\n",
    "    if MAP.contains(vtdGeom[v].centroid) and v not in parentVTDno:\n",
    "        print(\"Uhoh,\",v,\"is in the map but not in the parent vtd list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ecd50c96-51ce-4d83-b0df-e8c645c35815",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after above patching, write these contiguous lists to a file, converting to vtd lists\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter 1 if there were NO fragmented VTDs 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on full or partial VTD master list for unit 0\n",
      "working on full or partial VTD master list for unit 2000\n",
      "working on full or partial VTD master list for unit 4000\n",
      "Now converting unit lists to vtd lists for all HDs\n"
     ]
    }
   ],
   "source": [
    "#THIS WORKS FOR BOTH VTD- AND UNIT-BASED (FRAG VTD) -- **NOTE: DOES NOT ADD IN CUT DISTRICTS AND REBALANCE WEIGHTS\n",
    "print(\"after above patching, write these contiguous lists to a file, converting to vtd lists\")\n",
    "noFrag = int(input(\"enter 1 if there were NO fragmented VTDs\"))  #for simple states where units are at least whole vtd's\n",
    "tList = [t for t in range(nHDs)]\n",
    "vtdList = [list() for t in range(nHDs)]\n",
    "unitVTDlist, unitFragVTDlist, unitVTDfrac = [list() for u in range(nUnits)], [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "unitFullVTDlist, unitPartialVTDlist =       [list() for u in range(nUnits)], [list() for u in range(nUnits)]\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if u %2000 == 0:\n",
    "        print(\"working on full or partial VTD master list for unit\",u)\n",
    "    if allUnits[u] % 1 == 0.5 :\n",
    "        c = int(allUnits[u])\n",
    "        unitVTDlist[u] = countyTractList[c].copy()\n",
    "        unitFullVTDlist[u] = countyTractList[c].copy()\n",
    "    if allUnits[u] % 1 == 0.25 :\n",
    "        CCBnumber = int(allUnits[u])\n",
    "        for c in CCBlist[CCBnumber] :\n",
    "            unitVTDlist[u] += countyTractList[c]\n",
    "            unitFullVTDlist[u] += countyTractList[c]\n",
    "    if allUnits[u] % 1 == 0:\n",
    "        if noFrag == 1:\n",
    "            unitVTDlist[u].append(allUnits[u] )\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitVTDlist[u].append(surroundedVTDs[i])\n",
    "        else:\n",
    "            unitFragVTDlist[u].append(allUnits[u])\n",
    "            for i,vv in enumerate(surrounders):\n",
    "                if allUnits[u] == vv:\n",
    "                    unitFragVTDlist[u].append(surroundedVTDs[i])\n",
    "            vtdFrac = [0. for V in range(nVTDs)]\n",
    "            for vv in unitFragVTDlist[u]:\n",
    "                V = parentVTDno[vv]\n",
    "                if len(VTDchildren[V]) == 1:  #nonfragmented vtd\n",
    "                    vtdFrac[V] = 1.\n",
    "                else:\n",
    "                    if tractPop[V] > 0:\n",
    "                        vtdFrac[V] += fragVTDgeom[vv].area / vtdGeom[V].area #fragVTDpop[uu] / tractPop[v]  #we overwrote the frag pops earlier; can't use\n",
    "            for v in range(nVTDs):\n",
    "                if vtdFrac[v] > 0.0001:\n",
    "                    if vtdFrac[v] > 0.999:\n",
    "                        unitFullVTDlist[u].append(v)\n",
    "                    else:\n",
    "                        unitPartialVTDlist[u].append(v)\n",
    "                        unitVTDfrac[u].append(vtdFrac[v] )\n",
    "                                    \n",
    "HDpartialVTDlist, HDfullVTDlist, HDvtdFrac = [list() for t in range(nHDs)] ,[list() for t in range(nHDs)],[list() for t in range(nHDs)]               \n",
    "print(\"Now converting unit lists to vtd lists for all HDs\")\n",
    "if noFrag == 1:\n",
    "    for t in popHDlist:\n",
    "        for u in HDunitList[t]:\n",
    "            vtdList[t] += unitVTDlist[u]\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":vtdList,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "else:\n",
    "    for t in popHDlist:\n",
    "        partialList, partialFrac = list(), list()  #for many HDs, we'll recover whole VTDs when aggregating units\n",
    "        for u in HDunitList[t]:\n",
    "            HDfullVTDlist[t] +=   unitFullVTDlist[u] \n",
    "            partialList +=         unitPartialVTDlist[u]\n",
    "            partialFrac +=          unitVTDfrac[u]\n",
    "        partialSet = set(partialList)  #non-duplicated set of partials across the HD, prepping to aggregate where possible\n",
    "        partialSetFrac, partialSetList = [0. for v in partialSet], list(partialSet)\n",
    "        for i,v in enumerate(partialList):\n",
    "            partialSetFrac[partialSetList.index(v)] += partialFrac[i]\n",
    "        for i,v in enumerate(partialSetList):\n",
    "            if partialSetFrac[i] > 0.999:  #the district has all the fragments of the original VTD contained in the HD's units\n",
    "                HDfullVTDlist[t].append(v)\n",
    "            else:\n",
    "                HDpartialVTDlist[t].append(v)\n",
    "                HDvtdFrac[t].append(      r5(partialSetFrac[i]) )  #save the aggregated fraction of this VTD across all units\n",
    "    outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                           \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5075814f-4a75-4807-ad8d-b3cc969cb470",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6361 6361 6361 6361 6361 6361\n"
     ]
    }
   ],
   "source": [
    "\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDvPop = HDvPop[0:nHDs]\n",
    "HDweight = HDweight[0:nHDs]\n",
    "HDfullVTDlist = HDfullVTDlist[0:nHDs]\n",
    "HDpartialVTDlist = HDpartialVTDlist[0:nHDs]\n",
    "HDvtdFrac = HDvtdFrac[0:nHDs]\n",
    "hdCPx, hdCPy = hdCPx[0:nHDs], hdCPy[0:nHDs]\n",
    "print(nHDs,len(tList), len(HDweight), len(HDvPop), len(HDvtdFrac), len(hdCPx) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e385f357-5b7e-46f5-b054-c5c38d799395",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 6361\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight), nHDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c515b29-912e-4866-a05c-733749abb28e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#below not done for NYupper.  Instead, need to group NYu and NYl together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "313c7eca-aa6b-4fb9-8c45-9d63d090aaba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "8d922813-2854-41d7-9405-22db5d5d5a59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now append the cut districts and adjust the HDweights\n",
      "13 1 are the number of HD-drawn and cut districts\n"
     ]
    }
   ],
   "source": [
    "print(\"Now append the cut districts and adjust the HDweights\")\n",
    "print(nDistricts,nCutDistricts,\"are the number of HD-drawn and cut districts\")\n",
    "HDweight = [nDistricts/float(nCutDistricts + nDistricts) * HDweight[t] for t in range(nHDs)]\n",
    "tList = [t for t in range(nHDs)]\n",
    "if type(hdCPx) != type(list()):\n",
    "    hdCPx, hdCPy = hdCPx.to_list(), hdCPy.to_list()\n",
    "for L in allCutLists:\n",
    "    tList.append(len(tList))\n",
    "    HDweight.append(1./(nCutDistricts + nDistricts))\n",
    "    pop = np.sum([tractPop[v] for v in L])\n",
    "    HDvPop.append(pop )\n",
    "    HDfullVTDlist.append(L)\n",
    "    HDpartialVTDlist.append(list() )\n",
    "    HDvtdFrac.append(list() )\n",
    "    hdCPx.append(np.sum([tractPop[v]*tractCPx[v] for v in L]) / pop )\n",
    "    hdCPy.append(np.sum([tractPop[v]*tractCPy[v] for v in L]) / pop )\n",
    "    \n",
    "\n",
    "outDF = pd.DataFrame( {\"tract\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":HDfullVTDlist,\"partials\":HDpartialVTDlist,\n",
    "                        \"HDvtdFrac\":HDvtdFrac,\"centroid x\":hdCPx, \"centroid y\":hdCPy} )\n",
    "outname = STATE+str(int(nHDs))+\"patchedWithCutsVTDs.csv\"   #patchDown, PatchUp, PatchBoth\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b0b611f7-6c35-495d-b38c-c848317d63ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(HDweight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b9d808de-119f-4197-8e0c-8407c105d789",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vtdUSE = [0. for v in range(nVTDs)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitFullVTDlist[u]:\n",
    "        vtdUSE[v] += 1\n",
    "    for i,v in enumerate(unitPartialVTDlist[u]):\n",
    "        vtdUSE[v] += unitVTDfrac[u][i]\n",
    "\n",
    "plt.scatter([v for v in range(nVTDs)], vtdUSE)\n",
    "plt.show()\n",
    "unused = list()\n",
    "for v in range(nVTDs):\n",
    "    if vtdUSE[v] < 0.2 and MAP.contains(vtdGeom[v].centroid) and tractPop[v] > 0:\n",
    "        unused.append(v)\n",
    "        #print(v,\"in county\",countyNo[v],\"has pop\",tractPop[v],\"and map-total usage of\",vtdUSE[v],\"its surroundedness is\",v in surroundedVTDs)\n",
    "        plotPoly(vtdGeom[v].centroid.buffer(0.1))\n",
    "        plotCenter(v,vtdGeom[v],8)\n",
    "    #if vtdUSE[v] > 0.2 and vtdUSE[v] < 0.95:\n",
    "    #    plotCenter(r3(vtdUSE[v]),vtdGeom[v])\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3d2a393c-5ab8-4140-80eb-1153c2ef41f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u]  < 0.9:\n",
    "        plotPoly(unitGeom[u],0.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "    if  unitUse[u]  > 1.1:\n",
    "        plotPoly(unitGeom[u],1.2)\n",
    "        plotCenter(r3(unitUse[u]),unitGeom[u],6)\n",
    "plotPoly(MAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "404935e6-f0de-4b51-9db9-020aa208f852",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrity check: vtd-based pops\n",
      "x = n surrounded, y= unit - vtd-based\n"
     ]
    },
    {
     "data": {
      "image/png": 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h4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGCVrAaWhoUEFBgex2uxISEjRz5kw1Njb63Gf58uW64YYbZLfbZbPZdPTo0YC0CwAAzi1BCzgFBQXas2ePKioqtG7dOm3evFlFRUU+92lqalJeXp4ee+yxgLYLAADOLTbLsqxAN1pTU6O0tDRt375d48ePlySVl5crPz9fhw4dUkpKis/9P/74Y91444369ttvlZCQELB2T3G73YqPj5fL5ZLdbu/eIAEAQK/y5/M7KGdwKisrlZCQ4AkhkpSTk6OIiAht3bq119ttbm6W2+32WgAAgLmCEnCcTqeSk5O91kVFRSkxMVFOp7PX2y0tLVV8fLxnSU1N7XYfAABA3+dXwCkpKZHNZvO57N27N1h97bYFCxbI5XJ5loMHD4a6SwAAIIii/CmeP3++ZsyY4bNm+PDhcjgcqq+v91rf2tqqhoYGORwOvzt5SnfbjYmJUUxMTLdfFwAAhBe/Ak5SUpKSkpLOWpeVlaWjR4+qqqpK6enpkqSNGzeqvb1dmZmZ3etpENsFAABmCco9OGPGjFFeXp4KCwu1bds2ffLJJyouLtbUqVM9TzodPnxYo0eP1rZt2zz7OZ1OVVdXa//+/ZKkXbt2qbq6Wg0NDV1uFwAAIGjz4KxevVqjR49Wdna28vPzde2112r58uWe7S0tLdq3b5+ampo868rKyjR27FgVFhZKkq677jqNHTtW7777bpfbBQAACMo8OH0d8+AAABB+Qj4PDgAAQCgRcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4wQt4DQ0NKigoEB2u10JCQmaOXOmGhsbfe6zfPly3XDDDbLb7bLZbDp69OgZNUOHDpXNZvNaFi9eHKRRAACAcBS0gFNQUKA9e/aooqJC69at0+bNm1VUVORzn6amJuXl5emxxx7zWbdw4ULV1dV5lvvvvz+QXQcAAGEuKhiN1tTUqLy8XNu3b9f48eMlSS+99JLy8/P1wgsvKCUlpcP95s6dK0n6+OOPfbYfFxcnh8MRyC4DAACDBOUMTmVlpRISEjzhRpJycnIUERGhrVu39rj9xYsX67zzztPYsWP1/PPPq7W11Wd9c3Oz3G631wIAAMwVlDM4TqdTycnJ3i8UFaXExEQ5nc4etT1nzhyNGzdOiYmJ2rJlixYsWKC6ujotWbKk031KS0v1zDPP9Oh1AQBA+PDrDE5JSckZN/ievuzduzdYfZUkPfjgg7rhhht0xRVXaNasWfrZz36ml156Sc3NzZ3us2DBArlcLs9y8ODBoPYRAACEll9ncObPn68ZM2b4rBk+fLgcDofq6+u91re2tqqhoSHg985kZmaqtbVVX3zxhUaNGtVhTUxMjGJiYgL6ugAAoO/yK+AkJSUpKSnprHVZWVk6evSoqqqqlJ6eLknauHGj2tvblZmZ2b2edqK6uloRERFnXBIDAADnrqDcgzNmzBjl5eWpsLBQZWVlamlpUXFxsaZOnep5gurw4cPKzs7WqlWrlJGRIem7e3ecTqf2798vSdq1a5fi4uJ04YUXKjExUZWVldq6datuvPFGxcXFqbKyUvPmzdO0adM0aNCgYAwFAACEoaDNg7N69WqNHj1a2dnZys/P17XXXqvly5d7tre0tGjfvn1qamryrCsrK9PYsWNVWFgoSbruuus0duxYvfvuu5K+u9S0Zs0aXX/99br00kv17LPPat68eV7tAgAA2CzLskLdid7mdrsVHx8vl8slu90e6u4AAIAu8Ofzm++iAgAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjBDXgNDQ0qKCgQHa7XQkJCZo5c6YaGxt91t9///0aNWqU+vfvrwsvvFBz5syRy+Xyqvvqq680adIkDRgwQMnJyXr44YfV2toazKEAAIAwEhXMxgsKClRXV6eKigq1tLTo7rvvVlFRkV5//fUO62tra1VbW6sXXnhBaWlp+vLLLzVr1izV1tbqrbfekiS1tbVp0qRJcjgc2rJli+rq6nTnnXeqX79+eu6554I5HAAAECZslmVZwWi4pqZGaWlp2r59u8aPHy9JKi8vV35+vg4dOqSUlJQutfPmm29q2rRpOn78uKKiorR+/Xr9+Mc/Vm1trQYPHixJKisr06OPPqqvv/5a0dHRZ23T7XYrPj5eLpdLdru9+4MEAAC9xp/P76BdoqqsrFRCQoIn3EhSTk6OIiIitHXr1i63c2oQUVFRnnYvv/xyT7iRpNzcXLndbu3Zs6fDNpqbm+V2u70WAABgrqAFHKfTqeTkZK91UVFRSkxMlNPp7FIb33zzjRYtWqSioiKvdr8fbiR5fu6s3dLSUsXHx3uW1NRUf4YCAADCjN8Bp6SkRDabzeeyd+/eHnfM7XZr0qRJSktL09NPP92jthYsWCCXy+VZDh482OP+AQCAvsvvm4znz5+vGTNm+KwZPny4HA6H6uvrvda3traqoaFBDofD5/7Hjh1TXl6e4uLitHbtWvXr18+zzeFwaNu2bV71R44c8WzrSExMjGJiYny+JgAAMIffAScpKUlJSUlnrcvKytLRo0dVVVWl9PR0SdLGjRvV3t6uzMzMTvdzu93Kzc1VTEyM3n33XcXGxp7R7rPPPqv6+nrPJbCKigrZ7XalpaX5OxwAAGCgoN2DM2bMGOXl5amwsFDbtm3TJ598ouLiYk2dOtXzBNXhw4c1evRozxkZt9utiRMn6vjx4/qP//gPud1uOZ1OOZ1OtbW1SZImTpyotLQ0TZ8+XX/605+0YcMGPf7445o9ezZnaQAAgKQgz4OzevVqFRcXKzs7WxEREZoyZYqWLl3q2d7S0qJ9+/apqalJkrRjxw7PE1YjR470auvAgQMaOnSoIiMjtW7dOt17773KysrSD37wA911111auHBhMIcCAADCSNDmwenLmAcHAIDw0yfmwQEAAAgVAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYJygBpyGhgYVFBTIbrcrISFBM2fOVGNjo8/6+++/X6NGjVL//v114YUXas6cOXK5XF51NpvtjGXNmjXBHAoAAAgjUcFsvKCgQHV1daqoqFBLS4vuvvtuFRUV6fXXX++wvra2VrW1tXrhhReUlpamL7/8UrNmzVJtba3eeustr9qVK1cqLy/P83NCQkIwhwIAAMKIzbIsKxgN19TUKC0tTdu3b9f48eMlSeXl5crPz9ehQ4eUkpLSpXbefPNNTZs2TcePH1dU1Hd5zGazae3atZo8eXK3+uZ2uxUfHy+XyyW73d6tNgAAQO/y5/M7aJeoKisrlZCQ4Ak3kpSTk6OIiAht3bq1y+2cGsSpcHPK7Nmzdf755ysjI0MrVqyQr5zW3Nwst9vttQAAAHMF7RKV0+lUcnKy94tFRSkxMVFOp7NLbXzzzTdatGiRioqKvNYvXLhQN910kwYMGKAPP/xQ9913nxobGzVnzpwO2yktLdUzzzzTvYEAAICw4/cZnJKSkg5v8v3+snfv3h53zO12a9KkSUpLS9PTTz/tte2JJ57QNddco7Fjx+rRRx/VI488oueff77TthYsWCCXy+VZDh482OP+AQCAvsvvMzjz58/XjBkzfNYMHz5cDodD9fX1XutbW1vV0NAgh8Phc/9jx44pLy9PcXFxWrt2rfr16+ezPjMzU4sWLVJzc7NiYmLO2B4TE9PhegAAYCa/A05SUpKSkpLOWpeVlaWjR4+qqqpK6enpkqSNGzeqvb1dmZmZne7ndruVm5urmJgYvfvuu4qNjT3ra1VXV2vQoEGEGAAAICmI9+CMGTNGeXl5KiwsVFlZmVpaWlRcXKypU6d6nqA6fPiwsrOztWrVKmVkZMjtdmvixIlqamrSa6+95nVDcFJSkiIjI/Xee+/pyJEjmjBhgmJjY1VRUaHnnntODz30ULCGAgAAwkxQ58FZvXq1iouLlZ2drYiICE2ZMkVLly71bG9padG+ffvU1NQkSdqxY4fnCauRI0d6tXXgwAENHTpU/fr107JlyzRv3jxZlqWRI0dqyZIlKiwsDOZQAABAGAnaPDh9GfPgAAAQfvrEPDgAAAChQsABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYJasBpaGhQQUGB7Ha7EhISNHPmTDU2Nvrc55577tGIESPUv39/JSUl6dZbb9XevXu9ar766itNmjRJAwYMUHJysh5++GG1trYGcygAACCMBDXgFBQUaM+ePaqoqNC6deu0efNmFRUV+dwnPT1dK1euVE1NjTZs2CDLsjRx4kS1tbVJktra2jRp0iSdPHlSW7Zs0a9//Wu9+uqrevLJJ4M5FAAAEEZslmVZwWi4pqZGaWlp2r59u8aPHy9JKi8vV35+vg4dOqSUlJQutfPpp5/qyiuv1P79+zVixAitX79eP/7xj1VbW6vBgwdLksrKyvToo4/q66+/VnR09FnbdLvdio+Pl8vlkt1u7/4gAQBAr/Hn8ztoZ3AqKyuVkJDgCTeSlJOTo4iICG3durVLbRw/flwrV67UsGHDlJqa6mn38ssv94QbScrNzZXb7daePXs6bKe5uVlut9trAQAA5gpawHE6nUpOTvZaFxUVpcTERDmdTp/7vvzyyxo4cKAGDhyo9evXq6KiwnNmxul0eoUbSZ6fO2u3tLRU8fHxnuVUWAIAAGbyO+CUlJTIZrP5XE6/KdhfBQUF2rlzpzZt2qRLLrlEt912m06cONHt9hYsWCCXy+VZDh482KP+AQCAvi3K3x3mz5+vGTNm+KwZPny4HA6H6uvrvda3traqoaFBDofD5/6nzrRcfPHFmjBhggYNGqS1a9fqjjvukMPh0LZt27zqjxw5IkmdthsTE6OYmJizjAwAAJjC74CTlJSkpKSks9ZlZWXp6NGjqqqqUnp6uiRp48aNam9vV2ZmZpdfz7IsWZal5uZmT7vPPvus6uvrPZfAKioqZLfblZaW5u9wAACAgYJ2D86YMWOUl5enwsJCbdu2TZ988omKi4s1depUzxNUhw8f1ujRoz1nZD7//HOVlpaqqqpKX331lbZs2aJ/+qd/Uv/+/ZWfny9JmjhxotLS0jR9+nT96U9/0oYNG/T4449r9uzZnKUBAACSgjwPzurVqzV69GhlZ2crPz9f1157rZYvX+7Z3tLSon379qmpqUmSFBsbq9/97nfKz8/XyJEjdfvttysuLk5btmzxnK2JjIzUunXrFBkZqaysLE2bNk133nmnFi5cGMyhAACAMBK0eXD6MubBAQAg/PSJeXAAAABChYADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4UaHugElOtrbrN5Vf6MuGJl2UOEDTs4YqOooMCQBAbyPgBEjpB59p+eYDsr637qfv16joumFakJ8Wsn4BAHAuIuAEQOkHn+mVzQfOWG9JnvWEHAAAek9Qr580NDSooKBAdrtdCQkJmjlzphobG33uc88992jEiBHq37+/kpKSdOutt2rv3r1eNTab7YxlzZo1wRxKp062tmt5B+Hm+5ZvPqCTre291CMAABDUgFNQUKA9e/aooqJC69at0+bNm1VUVORzn/T0dK1cuVI1NTXasGGDLMvSxIkT1dbW5lW3cuVK1dXVeZbJkycHcSSd+/UW78tSHbH+VgcAAHqHzbKss30+d0tNTY3S0tK0fft2jR8/XpJUXl6u/Px8HTp0SCkpKV1q59NPP9WVV16p/fv3a8SIEd912mbT2rVrux1q3G634uPj5XK5ZLfbu9XGKf+w9GPtqD1+1rpxKT/Q/5tzQ49eCwCAc5k/n99BO4NTWVmphIQET7iRpJycHEVERGjr1q1dauP48eNauXKlhg0bptTUVK9ts2fP1vnnn6+MjAytWLFCvnJac3Oz3G631xIoXQk3/tQhdMq3H9bQkvc9S/n2w6HuEgCgm4IWcJxOp5KTk73WRUVFKTExUU6n0+e+L7/8sgYOHKiBAwdq/fr1qqioUHR0tGf7woUL9dvf/lYVFRWaMmWK7rvvPr300kudtldaWqr4+HjPcnpYAoaWvK9Zb1d7rZv1drWGlrwfmg4BAHrE74BTUlLS4U2+319OvynYXwUFBdq5c6c2bdqkSy65RLfddptOnDjh2f7EE0/ommuu0dixY/Xoo4/qkUce0fPPP99pewsWLJDL5fIsBw8e7FH/YJazhRhCDgCEH78fE58/f75mzJjhs2b48OFyOByqr6/3Wt/a2qqGhgY5HA6f+58603LxxRdrwoQJGjRokNauXas77rijw/rMzEwtWrRIzc3NiomJOWN7TExMh+uBrl6GKt9+WHk//Lsg9wYAECh+B5ykpCQlJSWdtS4rK0tHjx5VVVWV0tPTJUkbN25Ue3u7MjMzu/x6lmXJsiw1Nzd3WlNdXa1BgwYRYuC30y9L+ar7goADAGEjaBP9jRkzRnl5eSosLFRZWZlaWlpUXFysqVOnep6gOnz4sLKzs7Vq1SplZGTo888/1xtvvKGJEycqKSlJhw4d0uLFi9W/f3/l5+dLkt577z0dOXJEEyZMUGxsrCoqKvTcc8/poYceCtZQAABAmAnqTMarV69WcXGxsrOzFRERoSlTpmjp0qWe7S0tLdq3b5+ampokSbGxsfrd736nn//85/r22281ePBgXXfdddqyZYvnhuV+/fpp2bJlmjdvnizL0siRI7VkyRIVFhYGcygAACCMBG0enL4skPPg+HMD6heLJ/XotRB45dsPd+kyVdmUq7gHBwBCrE/MgwOEg66GFsINAIQXAg7OeWc7s8aZNwAIPwQcQN+FmLIpV3mtK5tyFeEGAMJUUG8yPhfc9/dD9fLvvuhSHfq2vB/+HY+CA4AhOIPTQ1kXJ5+9yI86AADQcwScHmpoOhnQOgAA0HMEnB5KjosNaB0AAOg5Ak4PZQxL1JD4WNk62W6TNCQ+VhnDEnuzWwAAnNMIOD0UGWHTU7ekSdIZIefUz0/dkqbIiM4iEAAACDQCTgDkXTZEv5o2To5478tQjvhY/WraOOVdNiREPQMA4NzEY+IBknfZEN2c5tC2Aw2qP3ZCyXHfXZbizA0AAL2PgBNAkRE2ZY04L9TdAADgnMclKgAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgnHNyJmPLsiRJbrc7xD0BAABddepz+9TnuC/nZMA5duyYJCk1NTXEPQEAAP46duyY4uPjfdbYrK7EIMO0t7ertrZWcXFxstkC+2WYbrdbqampOnjwoOx2e0Db7gsYX/gzfYyML/yZPkbTxycFb4yWZenYsWNKSUlRRITvu2zOyTM4ERERuuCCC4L6Gna73dhfXInxmcD0MTK+8Gf6GE0fnxScMZ7tzM0p3GQMAACMQ8ABAADGIeAEWExMjJ566inFxMSEuitBwfjCn+ljZHzhz/Qxmj4+qW+M8Zy8yRgAAJiNMzgAAMA4BBwAAGAcAg4AADAOAQcAABiHgNMNy5Yt09ChQxUbG6vMzExt27bNZ/2bb76p0aNHKzY2Vpdffrk++OCDXupp9/gzvldffVU2m81riY2N7cXe+mfz5s265ZZblJKSIpvNpnfeeees+3z88ccaN26cYmJiNHLkSL366qtB72d3+Tu+jz/++IzjZ7PZ5HQ6e6fDfiotLdUPf/hDxcXFKTk5WZMnT9a+ffvOul84vQe7M8Zweh/+6le/0hVXXOGZAC4rK0vr16/3uU84HT9/xxdOx64jixcvls1m09y5c33WheIYEnD89MYbb+jBBx/UU089pR07dujKK69Ubm6u6uvrO6zfsmWL7rjjDs2cOVM7d+7U5MmTNXnyZO3evbuXe941/o5P+m6myrq6Os/y5Zdf9mKP/XP8+HFdeeWVWrZsWZfqDxw4oEmTJunGG29UdXW15s6dq5/85CfasGFDkHvaPf6O75R9+/Z5HcPk5OQg9bBnNm3apNmzZ+sPf/iDKioq1NLSookTJ+r48eOd7hNu78HujFEKn/fhBRdcoMWLF6uqqkp//OMfddNNN+nWW2/Vnj17OqwPt+Pn7/ik8Dl2p9u+fbteeeUVXXHFFT7rQnYMLfglIyPDmj17tufntrY2KyUlxSotLe2w/rbbbrMmTZrktS4zM9O65557gtrP7vJ3fCtXrrTi4+N7qXeBJclau3atz5pHHnnEuvTSS73W3X777VZubm4QexYYXRnff//3f1uSrG+//bZX+hRo9fX1liRr06ZNndaE23vwdF0ZYzi/Dy3LsgYNGmT9+7//e4fbwv34WZbv8YXrsTt27Jh18cUXWxUVFdb1119vPfDAA53WhuoYcgbHDydPnlRVVZVycnI86yIiIpSTk6PKysoO96msrPSql6Tc3NxO60OpO+OTpMbGRl100UVKTU09618q4Sacjl9PXHXVVRoyZIhuvvlmffLJJ6HuTpe5XC5JUmJiYqc14X4MuzJGKTzfh21tbVqzZo2OHz+urKysDmvC+fh1ZXxSeB672bNna9KkSWccm46E6hgScPzwzTffqK2tTYMHD/ZaP3jw4E7vWXA6nX7Vh1J3xjdq1CitWLFC//mf/6nXXntN7e3tuvrqq3Xo0KHe6HLQdXb83G63/vrXv4aoV4EzZMgQlZWV6e2339bbb7+t1NRU3XDDDdqxY0eou3ZW7e3tmjt3rq655hpddtllndaF03vwdF0dY7i9D3ft2qWBAwcqJiZGs2bN0tq1a5WWltZhbTgeP3/GF27HTpLWrFmjHTt2qLS0tEv1oTqG5+S3iSNwsrKyvP4yufrqqzVmzBi98sorWrRoUQh7hq4YNWqURo0a5fn56quv1l/+8he9+OKL+s1vfhPCnp3d7NmztXv3bv3+978PdVeCpqtjDLf34ahRo1RdXS2Xy6W33npLd911lzZt2tRpCAg3/owv3I7dwYMH9cADD6iioqLP3wxNwPHD+eefr8jISB05csRr/ZEjR+RwODrcx+Fw+FUfSt0Z3+n69eunsWPHav/+/cHoYq/r7PjZ7Xb1798/RL0KroyMjD4fGoqLi7Vu3Tpt3rxZF1xwgc/acHoPfp8/YzxdX38fRkdHa+TIkZKk9PR0bd++Xb/4xS/0yiuvnFEbjsfPn/Gdrq8fu6qqKtXX12vcuHGedW1tbdq8ebN++ctfqrm5WZGRkV77hOoYconKD9HR0UpPT9dHH33kWdfe3q6PPvqo0+urWVlZXvWSVFFR4fN6bKh0Z3yna2tr065duzRkyJBgdbNXhdPxC5Tq6uo+e/wsy1JxcbHWrl2rjRs3atiwYWfdJ9yOYXfGeLpwex+2t7erubm5w23hdvw64mt8p+vrxy47O1u7du1SdXW1Zxk/frwKCgpUXV19RriRQngMg3oLs4HWrFljxcTEWK+++qr12WefWUVFRVZCQoLldDoty7Ks6dOnWyUlJZ76Tz75xIqKirJeeOEFq6amxnrqqaesfv36Wbt27QrVEHzyd3zPPPOMtWHDBusvf/mLVVVVZU2dOtWKjY219uzZE6oh+HTs2DFr586d1s6dOy1J1pIlS6ydO3daX375pWVZllVSUmJNnz7dU//5559bAwYMsB5++GGrpqbGWrZsmRUZGWmVl5eHagg++Tu+F1980XrnnXesP//5z9auXbusBx54wIqIiLD+67/+K1RD8Onee++14uPjrY8//tiqq6vzLE1NTZ6acH8PdmeM4fQ+LCkpsTZt2mQdOHDA+vTTT62SkhLLZrNZH374oWVZ4X/8/B1fOB27zpz+FFVfOYYEnG546aWXrAsvvNCKjo62MjIyrD/84Q+ebddff7111113edX/9re/tS655BIrOjrauvTSS63333+/l3vsH3/GN3fuXE/t4MGDrfz8fGvHjh0h6HXXnHos+vTl1Jjuuusu6/rrrz9jn6uuusqKjo62hg8fbq1cubLX+91V/o7vX/7lX6wRI0ZYsbGxVmJionXDDTdYGzduDE3nu6CjsUnyOibh/h7szhjD6X34z//8z9ZFF11kRUdHW0lJSVZ2drbnw9+ywv/4+Tu+cDp2nTk94PSVY2izLMsK7jkiAACA3sU9OAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAY5/8Dwt7zv73rwnIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"integrity check: vtd-based pops\")\n",
    "HDvtdbasedPop = [0. for t in range(nHDs)]\n",
    "HDvPop = [0. for t in range(nHDs)]\n",
    "nSurrs = [0. for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "        if u in surroundedVTDs:\n",
    "            nSurrs[t] += 1\n",
    "    for v in HDfullVTDlist[t]:\n",
    "        HDvtdbasedPop[t] += origVTDpop[v] #tractPop[v]\n",
    "    for i,v in enumerate(HDpartialVTDlist[t]):\n",
    "        HDvtdbasedPop[t] += HDvtdFrac[t][i] * origVTDpop[v] #tractPop[v] also works\n",
    "        \n",
    "plt.scatter([nSurrs[t] for t in popHDlist], [HDvPop[t] - HDvtdbasedPop[t] for t in popHDlist] )\n",
    "print(\"x = n surrounded, y= unit - vtd-based\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef27af54-daf5-4e8f-9230-bce5b0b1e705",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "3ad3342b-b8e3-46f5-a3e5-f49df386b88f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\n",
      "Let's read in the 2020 and 2016 voting data for NJ\n",
      "In year/election 2020 Dem and GOP total votes were 2607921 1882732 for a stateGOP of 0.41926\n",
      "There are a total of 6361  vote-mapped voting districts from election(s) in year  2020\n",
      "In year/election 2016 Dem and GOP total votes were 2148267 1601915 for a stateGOP of 0.42716\n",
      "There are a total of 6361  vote-mapped voting districts from election(s) in year  2016\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, read in the votes by vtd to compute ensemble vote margin vs. true state vote margin\")\n",
    "#note: we started w possibility of separate election --> 2020 vtd files by year.  Now we use ALARM combined 2016+2020 --> 2020 csv's for all exc CA\n",
    "# copied from \"HDpartisanAnalysis\"\n",
    "print(\"Let's read in the 2020 and 2016 voting data for\",STATE)\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G16PREDCLI\"], [\"G16PRERTRU\" ], list()\n",
    "#DemCandidates, RepCandidates, vestDFs = [\"G20PREDBID\", \"G16PREDCli\"], [\"G20PRERTRU\" ,\"G16PRERTru\" ], list()\n",
    "DemCandidates, RepCandidates, vestDFs = [\"pre_20_dem_bid\", \"pre_16_dem_cli\"], [\"pre_20_rep_tru\" ,\"pre_16_rep_tru\" ], list()\n",
    "nYears = 2\n",
    "unitIDs, vestReps,vestDems,yearLean = [list()]*nYears, [0]*nYears, [0]*nYears, [0.5]*nYears\n",
    "years = [str(2020),str(2016)]\n",
    "DemVotes, RepVotes = [list() for y in years], [list() for y in years]\n",
    "vestDir = \"./state_map_files/\" + STATE.lower()\n",
    "vestDF = pd.read_csv(vestDir + \"_2020_vtd.csv\")\n",
    "mergedDF = vestDF.copy() #pd.merge(mergedDF,vestDF, on = \"GEOID20\")\n",
    "for y,year in enumerate(years):\n",
    "    DemVotes[y], RepVotes[y], unitIDs[y] = mergedDF[DemCandidates[y]], mergedDF[RepCandidates[y]], vestDF[(\"GEOID20\")]\n",
    "    vestDems[y], vestReps[y] = np.sum(DemVotes[y]), np.sum(RepVotes[y])\n",
    "    yearLean[y] = vestReps[y]/float(vestDems[y]+vestReps[y])\n",
    "    print(\"In year/election\",year,\"Dem and GOP total votes were\",int(vestDems[y]),int(vestReps[y]),\"for a stateGOP of\",r5(yearLean[y]) )\n",
    "    nVTDs = len(DemVotes[y])\n",
    "    print(\"There are a total of\",nVTDs,\" vote-mapped voting districts from election(s) in year \",years[y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f628069d-4177-401e-bf49-b7f194d0ef8b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5cf73783-4a48-4a3f-a2e3-c1b9ff32c859",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's read in our ensemble of HD districts for NJ\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs NJ6361patchedVTDs.csv\n",
      "enter the number of HD-drawn districts in the state; e.g. 8 12\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ensemble or enacted map describes 6361 drawn districts.  This state has 12 districts per map\n",
      "converting vtd list strings to vtd lists by drawn district ...\n",
      "the total weight across all rows should be 1.000, actually is 1.0\n",
      "I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\n",
      "translating from HD order of precinct rows to VEST order\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's read in our ensemble of HD districts for\",STATE)\n",
    "infilename = input(\"enter the filename with the vtd assignments to districts; e.g. NC2666patchedWithCutsVTDs.csv\")\n",
    "\n",
    "HDdf = pd.read_csv(\"2024state_HD_output/\"+infilename)\n",
    "nRows = len(HDdf)\n",
    "nStateDistricts = int(input(\"enter the number of HD-drawn districts in the state; e.g. 8\"))\n",
    "print(\"This ensemble or enacted map describes\",nRows,\"drawn districts.  This state has\",nStateDistricts,\"districts per map\")\n",
    "\n",
    "HDvPop = HDdf[\"HDvPop\"].to_list()\n",
    "HDweight = HDdf['HDweight'].to_list()\n",
    "#tractPop = HDdf['tractPop'].to_list()\n",
    "#statePop = np.sum(tractPop)\n",
    "tractCPx = HDdf['centroid x'].to_list()\n",
    "tractCPy = HDdf['centroid y'].to_list()\n",
    "HDvtdListString = HDdf[\"HDvtdList\"]\n",
    "print(\"converting vtd list strings to vtd lists by drawn district ...\")\n",
    "HDvtdList = [list() for t in range(nRows) ]\n",
    "for t in range(nRows):\n",
    "    if HDvtdListString[t] != \"[]\":\n",
    "        HDvtdList[t] = ast.literal_eval(HDvtdListString[t])\n",
    "\n",
    "if \"partials\" in HDdf.columns.values: #\"splitTractNo\" #.to_list():\n",
    "    splitTractList, splitTractUseList = HDdf['partials'], HDdf['HDvtdFrac']\n",
    "    splitTractNo = [ast.literal_eval(splitTractList[t])    for t in range(nRows)]\n",
    "    splitTractUse = [ast.literal_eval(splitTractUseList[t]) for t in range(nRows)]\n",
    "else :  #incoming file didn't use any partial units, only whole ones\n",
    "    splitTractNo, splitTractUse = [[] for t in range(nRows)] , [ [] for t in range(nRows)]\n",
    "\n",
    "print(\"the total weight across all rows should be 1.000, actually is\",r5(np.sum(HDweight)) )\n",
    "print(\"I am assuming we already read in a 'tractPopFile' of Census vtd geoms & pops with a GEOID20 column\")\n",
    "censusGEOID20 = tractPopFile['GEOID20']\n",
    "miniHDdf = pd.DataFrame( {\"GEOID20\":censusGEOID20} )\n",
    "vestNo = [v for v in range(nVTDs)]\n",
    "print(\"translating from HD order of precinct rows to VEST order\")\n",
    "miniVestDF = pd.DataFrame( {\"GEOID20\":mergedDF['GEOID20'], \"vestNo\":vestNo} )\n",
    "mappingDF = pd.merge(miniHDdf,miniVestDF,on=\"GEOID20\")\n",
    "vestID = mappingDF['vestNo']  #this maps each named unit in a HDVL to the correct vestNo row with voting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "540c6d1d-7728-48bf-90f0-f124b25ac7c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sanity check - Dem-leaning vtds for year 2020\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"sanity check - Dem-leaning vtds for year\",years[0])  #for MA, do GOP-leaning.  Other states, use Dem-leaning\n",
    "for v in range(nVTDs):\n",
    "    plotPoly(vtdGeom[v],0.2)\n",
    "    if DemVotes[0][vestID[v]] > RepVotes[0][vestID[v]]:\n",
    "        plt.text(vtdGeom[v].centroid.x, vtdGeom[v].centroid.y, \"D\", color = \"blue\",fontsize=6)\n",
    "        #plotCenter(\"d\",vtdGeom[v],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "56ed7e9d-1400-4848-892e-737555552d58",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\n",
      "working on drawn district no 0\n",
      "working on drawn district no 1000\n",
      "working on drawn district no 2000\n",
      "working on drawn district no 3000\n",
      "working on drawn district no 4000\n",
      "working on drawn district no 5000\n",
      "working on drawn district no 6000\n",
      "here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\n",
      "true 2020: 2607921 1882732 -725189\n",
      "ensemble : 2610415 1883751 -726663\n",
      "the true and ensemble state GOP leans are 0.41926 0.41915482773204477\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, let's compute the ensemble average vote margin (GOP -  Dem total votes) for 2020 vs. true statewide result\")\n",
    "nDrawnDistricts = len(HDweight)  #now including cut districts\n",
    "nMapDistricts = nCutDistricts + nDistricts\n",
    "nEnsembleDems, nEnsembleReps = [0.]*nYears, [0.]*nYears\n",
    "y=0\n",
    "for t in range(nDrawnDistricts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on drawn district no\",t)\n",
    "    nEnsembleDems[y] += nMapDistricts* HDweight[t] * ( np.sum([DemVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([DemVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "    nEnsembleReps[y] += nMapDistricts* HDweight[t] * ( np.sum([RepVotes[y][vestID[v]] for v in HDvtdList[t] ])\n",
    "                                               + np.sum([RepVotes[y][vestID[v]] * splitTractUse[t][i] for i,v in enumerate(splitTractNo[t]) ])  )\n",
    "\n",
    "print(\"here is the table of Dem and Rep votes, vote margin for true 2020 vs. ensemble\")\n",
    "print(\"true 2020:\",int(vestDems[y]),int(vestReps[y]),              int(vestReps[y] - vestDems[y]) )\n",
    "print(\"ensemble :\",int(nEnsembleDems[y]),int(nEnsembleReps[y]),    int(nEnsembleReps[y] - nEnsembleDems[y]) )\n",
    "print(\"the true and ensemble state GOP leans are\",r5(vestReps[y]/(vestReps[y]+vestDems[y])),\n",
    "      nEnsembleReps[y]/(nEnsembleReps[y]+nEnsembleDems[y]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5c607c63-df0d-4334-8131-368c090c76d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total drawn districts, HD-drawn, map districts 6361 6361 12\n"
     ]
    }
   ],
   "source": [
    "print(\"total drawn districts, HD-drawn, map districts\",nDrawnDistricts,nHDs, nMapDistricts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d0d12-6bf4-4d5f-9236-fa8735393e98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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